ASAN Cholangio_GL_Final analysis 092024
library(swimplot) library(grid) library(gtable) library(readr) library(mosaic) library(dplyr) library(survival) library(survminer) library(ggplot2) library(scales) library(coxphf) library(ggthemes) library(tidyverse) library(gtsummary) library(flextable) library(parameters) library(car) library(ComplexHeatmap) library(rms)
#ctDNA positivity by stage and window
#ctDNA at MRD
rm(list=ls())
setwd("~/Downloads")
circ_data <- read.csv("ASAN_ClinicalData_GL_082023.csv")
circ_data <- circ_data[circ_data$ctDNA.MRD!="",]
circ_data$ctDNA.MRD <- factor(circ_data$ctDNA.MRD, levels=c("NEGATIVE","POSITIVE"))
circ_data <- subset(circ_data, ctDNA.MRD %in% c("NEGATIVE", "POSITIVE"))
circ_data$Stage <- factor(circ_data$Stage, levels=c("II","III","IV"))
positive_counts_by_stage <- aggregate(circ_data$ctDNA.MRD == "POSITIVE", by=list(circ_data$Stage), FUN=sum)
total_counts_by_stage <- aggregate(circ_data$ctDNA.MRD, by=list(circ_data$Stage), FUN=length)
combined_data <- data.frame(
Stage = total_counts_by_stage$Group.1,
Total_Count = total_counts_by_stage$x,
Positive_Count = positive_counts_by_stage$x,
Rate = (positive_counts_by_stage$x / total_counts_by_stage$x) * 100 # Convert to percentage
)
combined_data$Rate <- sprintf("%.2f%%", combined_data$Rate)
overall_total_count <- nrow(circ_data)
overall_positive_count <- nrow(circ_data[circ_data$ctDNA.MRD == "POSITIVE",])
overall_positivity_rate <- (overall_positive_count / overall_total_count) * 100 # Convert to percentage
overall_row <- data.frame(
Stage = "Overall",
Total_Count = overall_total_count,
Positive_Count = overall_positive_count,
Rate = sprintf("%.2f%%", overall_positivity_rate)
)
combined_data <- rbind(combined_data, overall_row)
print(combined_data)
#ctDNA C5D1
rm(list=ls())
setwd("~/Downloads")
circ_data <- read.csv("ASAN_ClinicalData_GL_082023.csv")
circ_data <- circ_data[circ_data$ctDNA.C5D1!="",]
circ_data$ctDNA.C5D1 <- factor(circ_data$ctDNA.C5D1, levels=c("NEGATIVE","POSITIVE"))
circ_data <- subset(circ_data, ctDNA.C5D1 %in% c("NEGATIVE", "POSITIVE"))
circ_data$Stage <- factor(circ_data$Stage, levels=c("II","III","IV"))
positive_counts_by_stage <- aggregate(circ_data$ctDNA.C5D1 == "POSITIVE", by=list(circ_data$Stage), FUN=sum)
total_counts_by_stage <- aggregate(circ_data$ctDNA.C5D1, by=list(circ_data$Stage), FUN=length)
combined_data <- data.frame(
Stage = total_counts_by_stage$Group.1,
Total_Count = total_counts_by_stage$x,
Positive_Count = positive_counts_by_stage$x,
Rate = (positive_counts_by_stage$x / total_counts_by_stage$x) * 100 # Convert to percentage
)
combined_data$Rate <- sprintf("%.2f%%", combined_data$Rate)
overall_total_count <- nrow(circ_data)
overall_positive_count <- nrow(circ_data[circ_data$ctDNA.C5D1 == "POSITIVE",])
overall_positivity_rate <- (overall_positive_count / overall_total_count) * 100 # Convert to percentage
overall_row <- data.frame(
Stage = "Overall",
Total_Count = overall_total_count,
Positive_Count = overall_positive_count,
Rate = sprintf("%.2f%%", overall_positivity_rate)
)
combined_data <- rbind(combined_data, overall_row)
print(combined_data)
#ctDNA C8D1
rm(list=ls())
setwd("~/Downloads")
circ_data <- read.csv("ASAN_ClinicalData_GL_082023.csv")
circ_data <- circ_data[circ_data$ctDNA.C8D1!="",]
circ_data$ctDNA.C8D1 <- factor(circ_data$ctDNA.C8D1, levels=c("NEGATIVE","POSITIVE"))
circ_data <- subset(circ_data, ctDNA.C8D1 %in% c("NEGATIVE", "POSITIVE"))
circ_data$Stage <- factor(circ_data$Stage, levels=c("II","III","IV"))
positive_counts_by_stage <- aggregate(circ_data$ctDNA.C8D1 == "POSITIVE", by=list(circ_data$Stage), FUN=sum)
total_counts_by_stage <- aggregate(circ_data$ctDNA.C8D1, by=list(circ_data$Stage), FUN=length)
combined_data <- data.frame(
Stage = total_counts_by_stage$Group.1,
Total_Count = total_counts_by_stage$x,
Positive_Count = positive_counts_by_stage$x,
Rate = (positive_counts_by_stage$x / total_counts_by_stage$x) * 100 # Convert to percentage
)
combined_data$Rate <- sprintf("%.2f%%", combined_data$Rate)
overall_total_count <- nrow(circ_data)
overall_positive_count <- nrow(circ_data[circ_data$ctDNA.C8D1 == "POSITIVE",])
overall_positivity_rate <- (overall_positive_count / overall_total_count) * 100 # Convert to percentage
overall_row <- data.frame(
Stage = "Overall",
Total_Count = overall_total_count,
Positive_Count = overall_positive_count,
Rate = sprintf("%.2f%%", overall_positivity_rate)
)
combined_data <- rbind(combined_data, overall_row)
print(combined_data)
#ctDNA EOT
rm(list=ls())
setwd("~/Downloads")
circ_data <- read.csv("ASAN_ClinicalData_GL_082023.csv")
circ_data <- circ_data[circ_data$ctDNA.EOT!="",]
circ_data$ctDNA.EOT <- factor(circ_data$ctDNA.EOT, levels=c("NEGATIVE","POSITIVE"))
circ_data <- subset(circ_data, ctDNA.EOT %in% c("NEGATIVE", "POSITIVE"))
circ_data$Stage <- factor(circ_data$Stage, levels=c("II","III","IV"))
positive_counts_by_stage <- aggregate(circ_data$ctDNA.EOT == "POSITIVE", by=list(circ_data$Stage), FUN=sum)
total_counts_by_stage <- aggregate(circ_data$ctDNA.EOT, by=list(circ_data$Stage), FUN=length)
combined_data <- data.frame(
Stage = total_counts_by_stage$Group.1,
Total_Count = total_counts_by_stage$x,
Positive_Count = positive_counts_by_stage$x,
Rate = (positive_counts_by_stage$x / total_counts_by_stage$x) * 100 # Convert to percentage
)
combined_data$Rate <- sprintf("%.2f%%", combined_data$Rate)
overall_total_count <- nrow(circ_data)
overall_positive_count <- nrow(circ_data[circ_data$ctDNA.EOT == "POSITIVE",])
overall_positivity_rate <- (overall_positive_count / overall_total_count) * 100 # Convert to percentage
overall_row <- data.frame(
Stage = "Overall",
Total_Count = overall_total_count,
Positive_Count = overall_positive_count,
Rate = sprintf("%.2f%%", overall_positivity_rate)
)
combined_data <- rbind(combined_data, overall_row)
print(combined_data)
#ctDNA anytime
rm(list=ls())
setwd("~/Downloads")
circ_data <- read.csv("ASAN_ClinicalData_GL_082023.csv")
circ_data <- circ_data[circ_data$ctDNA.anytime!="",]
circ_data$ctDNA.anytime <- factor(circ_data$ctDNA.anytime, levels=c("NEGATIVE","POSITIVE"))
circ_data <- subset(circ_data, ctDNA.anytime %in% c("NEGATIVE", "POSITIVE"))
circ_data$Stage <- factor(circ_data$Stage, levels=c("II","III","IV"))
positive_counts_by_stage <- aggregate(circ_data$ctDNA.anytime == "POSITIVE", by=list(circ_data$Stage), FUN=sum)
total_counts_by_stage <- aggregate(circ_data$ctDNA.anytime, by=list(circ_data$Stage), FUN=length)
combined_data <- data.frame(
Stage = total_counts_by_stage$Group.1,
Total_Count = total_counts_by_stage$x,
Positive_Count = positive_counts_by_stage$x,
Rate = (positive_counts_by_stage$x / total_counts_by_stage$x) * 100 # Convert to percentage
)
combined_data$Rate <- sprintf("%.2f%%", combined_data$Rate)
overall_total_count <- nrow(circ_data)
overall_positive_count <- nrow(circ_data[circ_data$ctDNA.anytime == "POSITIVE",])
overall_positivity_rate <- (overall_positive_count / overall_total_count) * 100 # Convert to percentage
overall_row <- data.frame(
Stage = "Overall",
Total_Count = overall_total_count,
Positive_Count = overall_positive_count,
Rate = sprintf("%.2f%%", overall_positivity_rate)
)
combined_data <- rbind(combined_data, overall_row)
print(combined_data)#Heatmap for clinicopathologic factors
rm(list=ls())
setwd("~/Downloads")
circ_data <- read.csv("ASAN_ClinicalData_GL_082023.csv")
circ_datadf <- as.data.frame(circ_data)
circ_data <- circ_data %>% arrange(Stage)
circ_datadf <- as.data.frame(circ_data)
ha <- HeatmapAnnotation(
Stage = circ_data$Stage,
Sex = circ_data$Sex,
PrimSite = circ_data$PrimSite,
pT = circ_data$pT,
Pathology = circ_data$Pathology,
Chemo = circ_data$Chemo,
ctDNA.MRD = circ_data$ctDNA.MRD,
ctDNA.C5D1 = circ_data$ctDNA.C5D1,
ctDNA.C8D1 = circ_data$ctDNA.C8D1,
ctDNA.EOT = circ_data$ctDNA.EOT,
ctDNA.anytime = circ_data$ctDNA.anytime,
RecStatus = circ_data$RecStatus,
VitalStatus = circ_data$VitalStatus,
col = list(Stage = c("II" = "seagreen2", "III" = "orange", "IV" = "purple"),
Sex = c("Female" = "goldenrod" , "Male" = "blue4"),
PrimSite = c("pCCA" = "darkgreen", "dCCA" ="#008BCE"),
pT = c("T1" = "lightblue", "T2" ="orange", "T3" = "brown" ),
Pathology = c("G1" = "yellow3", "G2" ="darkgreen", "G3" = "brown2"),
Chemo = c("CAP" = "lightblue", "GemCis" = "orange2"),
ctDNA.MRD = c("POSITIVE" = "red3", "NEGATIVE" ="blue"),
ctDNA.C5D1 = c("POSITIVE" = "red3", "NEGATIVE" ="blue"),
ctDNA.C8D1 = c("POSITIVE" = "red3", "NEGATIVE" ="blue"),
ctDNA.EOT = c("POSITIVE" = "red3", "NEGATIVE" ="blue"),
ctDNA.anytime = c("POSITIVE" = "red3", "NEGATIVE" ="blue"),
RFS.Event = c("TRUE" = "red3", "FALSE" ="blue"),
OS.Event = c("TRUE" = "black", "FALSE" ="grey")
)
)
ht <- Heatmap(matrix(nrow = 0, ncol = length(circ_data$Stage)),show_row_names = FALSE,cluster_rows = F,cluster_columns = FALSE, top_annotation = ha)
pdf("heatmap.pdf",width = 7, height = 7)
draw(ht, annotation_legend_side = "bottom")
dev.off()null device
1 #Prognostic role of ctDNA at the MRD time point
rm(list=ls())
setwd("~/Downloads")
circ_data <- read.csv("ASAN_ClinicalData_GL_082023.csv")
circ_data$RFS.months=circ_data$RFS.months-2
circ_data <- circ_data[circ_data$RFS.months>=0,]
circ_data$RFS.months[circ_data$RFS.months > 60] <- 60
circ_datadf <- as.data.frame(circ_data)
survfit(Surv(time = circ_data$RFS.months, event = circ_data$RFS.Event)~ctDNA.MRD, data = circ_data)Call: survfit(formula = Surv(time = circ_data$RFS.months, event = circ_data$RFS.Event) ~
ctDNA.MRD, data = circ_data)
n events median 0.95LCL 0.95UCL
ctDNA.MRD=NEGATIVE 67 45 15.84 11.96 27.6
ctDNA.MRD=POSITIVE 22 20 8.23 7.86 15.5event_summary <- circ_data %>%
group_by(ctDNA.MRD) %>%
summarise(
Total = n(),
Events = sum(RFS.Event),
Fraction = Events / n(),
Percentage = (Events / n()) * 100
)
print(event_summary)surv_object <-Surv(time = circ_data$RFS.months, event = circ_data$RFS.Event)
KM_curve <- survfit(surv_object ~ ctDNA.MRD, data = circ_data,conf.int=0.95,conf.type="log-log")
ggsurvplot(KM_curve, data = circ_data, pval = FALSE, conf.int = FALSE, risk.table = TRUE, break.time.by=12, palette=c("blue","red"), title="DFS - ctDNA MRD timepoint", ylab= "Disease Free Survival", xlab="Time (Months)", legend.labs=c("ctDNA Negative", "ctDNA Positive"), legend.title="")
summary(KM_curve, times= c(12, 24))Call: survfit(formula = surv_object ~ ctDNA.MRD, data = circ_data,
conf.int = 0.95, conf.type = "log-log")
ctDNA.MRD=NEGATIVE
time n.risk n.event survival std.err lower 95% CI upper 95% CI
12 38 28 0.577 0.0607 0.450 0.686
24 26 11 0.407 0.0608 0.288 0.523
ctDNA.MRD=POSITIVE
time n.risk n.event survival std.err lower 95% CI upper 95% CI
12 7 15 0.318 0.0993 0.1418 0.511
24 5 2 0.227 0.0893 0.0827 0.414circ_data$ctDNA.MRD <- factor(circ_data$ctDNA.MRD, levels=c("NEGATIVE","POSITIVE"))
cox_fit <- coxph(surv_object ~ ctDNA.MRD, data=circ_data)
ggforest(cox_fit,data = circ_data) 
summary(cox_fit)Call:
coxph(formula = surv_object ~ ctDNA.MRD, data = circ_data)
n= 89, number of events= 65
coef exp(coef) se(coef) z Pr(>|z|)
ctDNA.MRDPOSITIVE 0.5902 1.8043 0.2705 2.182 0.0291 *
---
Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1
exp(coef) exp(-coef) lower .95 upper .95
ctDNA.MRDPOSITIVE 1.804 0.5542 1.062 3.066
Concordance= 0.556 (se = 0.028 )
Likelihood ratio test= 4.38 on 1 df, p=0.04
Wald test = 4.76 on 1 df, p=0.03
Score (logrank) test = 4.9 on 1 df, p=0.03cox_fit_summary <- summary(cox_fit)
# Extract values for HR, 95% CI, and p-value
HR <- cox_fit_summary$coefficients[2]
lower_CI <- cox_fit_summary$conf.int[3]
upper_CI <- cox_fit_summary$conf.int[4]
p_value <- cox_fit_summary$coefficients[5]
label_text <- paste0("HR = ", round(HR, 2), " (", round(lower_CI, 2), "-", round(upper_CI, 2), "); p = ", round(p_value, 3))
print(label_text)[1] "HR = 1.8 (1.06-3.07); p = 0.029"#OS by ctDNA at the MRD time point
rm(list=ls())
setwd("~/Downloads")
circ_data <- read.csv("ASAN_ClinicalData_GL_082023.csv")
circ_data$OS.months=circ_data$OS.months-2
circ_data <- circ_data[circ_data$OS.months>=0,]
circ_datadf <- as.data.frame(circ_data)
survfit(Surv(time = circ_data$OS.months, event = circ_data$OS.Event)~ctDNA.MRD, data = circ_data)Call: survfit(formula = Surv(time = circ_data$OS.months, event = circ_data$OS.Event) ~
ctDNA.MRD, data = circ_data)
n events median 0.95LCL 0.95UCL
ctDNA.MRD=NEGATIVE 67 39 38.5 28.9 NA
ctDNA.MRD=POSITIVE 22 17 30.8 22.6 NAevent_summary <- circ_data %>%
group_by(ctDNA.MRD) %>%
summarise(
Total = n(),
Events = sum(OS.Event),
Fraction = Events / n(),
Percentage = (Events / n()) * 100
)
print(event_summary)surv_object <-Surv(time = circ_data$OS.months, event = circ_data$OS.Event)
KM_curve <- survfit(surv_object ~ ctDNA.MRD, data = circ_data,conf.int=0.95,conf.type="log-log")
ggsurvplot(KM_curve, data = circ_data, pval = FALSE, conf.int = FALSE, risk.table = TRUE, break.time.by=12, palette=c("blue","red"), title="OS - ctDNA MRD timepoint", ylab= "Overall Survival", xlab="Time (Months)", legend.labs=c("ctDNA Negative", "ctDNA Positive"), legend.title="")
summary(KM_curve, times= c(12, 24))Call: survfit(formula = surv_object ~ ctDNA.MRD, data = circ_data,
conf.int = 0.95, conf.type = "log-log")
ctDNA.MRD=NEGATIVE
time n.risk n.event survival std.err lower 95% CI upper 95% CI
12 65 2 0.970 0.0208 0.886 0.992
24 50 15 0.746 0.0532 0.624 0.834
ctDNA.MRD=POSITIVE
time n.risk n.event survival std.err lower 95% CI upper 95% CI
12 17 5 0.773 0.0893 0.537 0.898
24 14 3 0.636 0.1026 0.403 0.799circ_data$ctDNA.MRD <- factor(circ_data$ctDNA.MRD, levels=c("NEGATIVE","POSITIVE"))
cox_fit <- coxph(surv_object ~ ctDNA.MRD, data=circ_data)
ggforest(cox_fit,data = circ_data) 
summary(cox_fit)Call:
coxph(formula = surv_object ~ ctDNA.MRD, data = circ_data)
n= 89, number of events= 56
coef exp(coef) se(coef) z Pr(>|z|)
ctDNA.MRDPOSITIVE 0.4209 1.5234 0.2911 1.446 0.148
exp(coef) exp(-coef) lower .95 upper .95
ctDNA.MRDPOSITIVE 1.523 0.6564 0.861 2.695
Concordance= 0.54 (se = 0.032 )
Likelihood ratio test= 1.97 on 1 df, p=0.2
Wald test = 2.09 on 1 df, p=0.1
Score (logrank) test = 2.12 on 1 df, p=0.1cox_fit_summary <- summary(cox_fit)
# Extract values for HR, 95% CI, and p-value
HR <- cox_fit_summary$coefficients[2]
lower_CI <- cox_fit_summary$conf.int[3]
upper_CI <- cox_fit_summary$conf.int[4]
p_value <- cox_fit_summary$coefficients[5]
label_text <- paste0("HR = ", round(HR, 2), " (", round(lower_CI, 2), "-", round(upper_CI, 2), "); p = ", round(p_value, 3))
print(label_text)[1] "HR = 1.52 (0.86-2.7); p = 0.148"#Association of ctDNA MRD MTM levels with clinicopathological factors
rm(list=ls())
setwd("~/Downloads")
circ_data <- read.csv("ASAN_ClinicalData_GL_082023.csv")
tally(~pN, data=circ_data, margins = TRUE)pN
N1 N2 Total
69 20 89 circ_data$pN <- factor(circ_data$pN, levels = c("N1","N2"), labels = c("pN1 (n=69)","pN2 (n=20)"))
boxplot(ctDNA.MRD.MTM~pN, data=circ_data, main="ctDNA MRD MTM - pN", xlab="pN", ylab="MTM/mL", col="white",border="black", ylim=c(0, 10))
m1<-wilcox.test(ctDNA.MRD.MTM ~ pN, data=circ_data, na.rm=TRUE, exact=FALSE, conf.int=TRUE)
print(m1)
Wilcoxon rank sum test with continuity correction
data: ctDNA.MRD.MTM by pN
W = 441.5, p-value = 0.001285
alternative hypothesis: true location shift is not equal to 0
95 percent confidence interval:
-3.700528e-01 -2.534813e-05
sample estimates:
difference in location
-4.5909e-05 tally(~ResMarg, data=circ_data, margins = TRUE)ResMarg
R0 R1 Total
60 29 89 circ_data$ResMarg <- factor(circ_data$ResMarg, levels = c("R0","R1"), labels = c("R0 (n=60)","R1 (n=29)"))
boxplot(ctDNA.MRD.MTM~ResMarg, data=circ_data, main="ctDNA MRD MTM - Resection Margin",
xlab="ResMarg", ylab="MTM/mL", col="white", border="black", ylim=c(0, 10))
m2 <- wilcox.test(ctDNA.MRD.MTM ~ ResMarg, data=circ_data, na.rm=TRUE, exact=FALSE, conf.int=TRUE)
print(m2)
Wilcoxon rank sum test with continuity correction
data: ctDNA.MRD.MTM by ResMarg
W = 637.5, p-value = 0.007319
alternative hypothesis: true location shift is not equal to 0
95 percent confidence interval:
-0.1700429119 -0.0000566823
sample estimates:
difference in location
-7.789783e-05 tally(~PrimSite, data=circ_data, margins = TRUE)PrimSite
dCCA pCCA Total
46 43 89 circ_data$PrimSite <- factor(circ_data$PrimSite, levels = c("pCCA","dCCA"), labels = c("pCCA (n=43)","dCCA (n=46)"))
boxplot(ctDNA.MRD.MTM~PrimSite, data=circ_data, main="ctDNA MRD MTM - Primary Site",
xlab="Primary Site", ylab="MTM/mL", col="white", border="black", ylim=c(0, 10))
m3 <- wilcox.test(ctDNA.MRD.MTM ~ PrimSite, data=circ_data, na.rm=TRUE, exact=FALSE, conf.int=TRUE)
print(m3)
Wilcoxon rank sum test with continuity correction
data: ctDNA.MRD.MTM by PrimSite
W = 996, p-value = 0.9438
alternative hypothesis: true location shift is not equal to 0
95 percent confidence interval:
-2.850159e-05 9.561474e-06
sample estimates:
difference in location
4.711544e-05 tally(~Stage, data=circ_data, margins = TRUE)Stage
II III IV Total
38 39 12 89 circ_data$ctDNA.MRD.MTM <- as.numeric(as.character(circ_data$ctDNA.MRD.MTM))
circ_data$Stage <- factor(circ_data$Stage, levels = c("II","III","IV"), labels = c("II (n=37)", "III (n=40)","IV (n=12)"))
boxplot(ctDNA.MRD.MTM~Stage, data=circ_data, main="ctDNA MRD MTM - Stage",
xlab="Stage", ylab="MTM/mL", col="white", border="black", ylim=c(0, 10))
kruskal_result <- kruskal.test(ctDNA.MRD.MTM ~ Stage, data=circ_data)
print(kruskal_result)
Kruskal-Wallis rank sum test
data: ctDNA.MRD.MTM by Stage
Kruskal-Wallis chi-squared = 2.8384, df = 2, p-value = 0.2419pairwise_wilcox <- pairwise.wilcox.test(circ_data$ctDNA.MRD.MTM, circ_data$Stage,
p.adjust.method = "BH", na.rm = TRUE)Warning in wilcox.test.default(xi, xj, paired = paired, ...) :
cannot compute exact p-value with ties
Warning in wilcox.test.default(xi, xj, paired = paired, ...) :
cannot compute exact p-value with ties
Warning in wilcox.test.default(xi, xj, paired = paired, ...) :
cannot compute exact p-value with tiesprint(pairwise_wilcox)
Pairwise comparisons using Wilcoxon rank sum test with continuity correction
data: circ_data$ctDNA.MRD.MTM and circ_data$Stage
II (n=37) III (n=40)
III (n=40) 0.46 -
IV (n=12) 0.30 0.39
P value adjustment method: BH tally(~CA19.MRD, data=circ_data, margins = TRUE)CA19.MRD
Elevated Normal Total
15 74 89 circ_data$CA19.MRD <- factor(circ_data$CA19.MRD, levels = c("Normal","Elevated"), labels = c("Normal (n=74)","Elevated (n=15)"))
boxplot(ctDNA.MRD.MTM~CA19.MRD, data=circ_data, main="ctDNA MRD MTM - CA 19-9",
xlab="CA 19-9", ylab="MTM/mL", col="white", border="black", ylim=c(0, 10))
m4 <- wilcox.test(ctDNA.MRD.MTM ~ CA19.MRD, data=circ_data, na.rm=TRUE, exact=FALSE, conf.int=TRUE)
print(m4)
Wilcoxon rank sum test with continuity correction
data: ctDNA.MRD.MTM by CA19.MRD
W = 371.5, p-value = 0.00808
alternative hypothesis: true location shift is not equal to 0
95 percent confidence interval:
-2.000269e-01 -4.645475e-05
sample estimates:
difference in location
-5.786662e-06 #Median MTM/mL levels for ctDNA positive pts at MRD
rm(list=ls())
setwd("~/Downloads")
circ_data <- read.csv("ASAN_ClinicalData_GL_082023.csv")
circ_data <- circ_data[circ_data$ctDNA.MRD=="POSITIVE",]
median_ctDNA <- median(circ_data$ctDNA.MRD.MTM, na.rm = TRUE)
range_ctDNA <- range(circ_data$ctDNA.MRD.MTM, na.rm = TRUE)
cat("Median MTM/mL post-surgery:", median_ctDNA, "\n")Median MTM/mL post-surgery: 0.605 cat("Range MTM/mL post-surgery:", range_ctDNA[1], "-", range_ctDNA[2], "\n")Range MTM/mL post-surgery: 0.1 - 73.04 #Association of ctDNA MRD status with clinicopathological factors
rm(list=ls())
setwd("~/Downloads")
circ_data <- read.csv("ASAN_ClinicalData_GL_082023.csv")
circ_data$ctDNA.MRD <- factor(circ_data$ctDNA.MRD, levels = c("NEGATIVE", "POSITIVE"), labels = c("ctDNA(-)", "ctDNA(+)"))
circ_data$pN <- factor(circ_data$pN, levels = c("N1", "N2"), labels = c("pN1", "pN2"))
contingency_table <- table(circ_data$pN, circ_data$ctDNA.MRD)
chi_square_test <- chisq.test(contingency_table)Warning in chisq.test(contingency_table) :
Chi-squared approximation may be incorrectprint(chi_square_test)
Pearson's Chi-squared test with Yates' continuity correction
data: contingency_table
X-squared = 7.1944, df = 1, p-value = 0.007313fisher_exact_test <- fisher.test(contingency_table)
print(fisher_exact_test)
Fisher's Exact Test for Count Data
data: contingency_table
p-value = 0.006385
alternative hypothesis: true odds ratio is not equal to 1
95 percent confidence interval:
1.403411 15.830714
sample estimates:
odds ratio
4.647239 print(contingency_table)
ctDNA(-) ctDNA(+)
pN1 57 12
pN2 10 10table_df <- as.data.frame(contingency_table)
table_df$Total <- ave(table_df$Freq, table_df$Var1, FUN = sum)
table_df$Percentage <- table_df$Freq / table_df$Total
table_df$MiddlePercentage <- table_df$Percentage / 2
ggplot(table_df, aes(x = Var1, y = Percentage, fill = Var2)) +
geom_bar(stat = "identity") +
geom_text(aes(y = MiddlePercentage, label = Freq), position = "stack", color = "black", vjust = 1.5, size = 7) +
theme_minimal() +
labs(title = "ctDNA status - pN",
x = "pN",
y = "Patients (%)",
fill = "ctDNA MRD",
caption = paste("Fisher's exact test p-value: ", format.pval(fisher_exact_test$p.value))) +
scale_y_continuous(labels = scales::percent_format()) +
scale_fill_manual(values = c("ctDNA(-)" = "blue", "ctDNA(+)" = "red")) + # define custom colors
theme(axis.text.x = element_text(angle = 0, hjust = 1.5, size = 14), # increase x-axis text size
axis.text.y = element_text(size = 14, color = "black"), # increase y-axis text size
axis.title.x = element_text(size = 14, color = "black"), # increase x-axis label size
axis.title.y = element_text(size = 14, color = "black"), # increase y-axis label size
legend.text = element_text(size = 12, color = "black")) # increase Progression label size
rm(list=ls())
setwd("~/Downloads")
circ_data <- read.csv("ASAN_ClinicalData_GL_082023.csv")
circ_data$ctDNA.MRD <- factor(circ_data$ctDNA.MRD, levels = c("NEGATIVE", "POSITIVE"), labels = c("ctDNA(-)", "ctDNA(+)"))
circ_data$ResMarg <- factor(circ_data$ResMarg, levels = c("R0", "R1"), labels = c("R0", "R1"))
contingency_table <- table(circ_data$ResMarg, circ_data$ctDNA.MRD)
chi_square_test <- chisq.test(contingency_table)
print(chi_square_test)
Pearson's Chi-squared test with Yates' continuity correction
data: contingency_table
X-squared = 5.1569, df = 1, p-value = 0.02315fisher_exact_test <- fisher.test(contingency_table)
print(fisher_exact_test)
Fisher's Exact Test for Count Data
data: contingency_table
p-value = 0.01762
alternative hypothesis: true odds ratio is not equal to 1
95 percent confidence interval:
1.146895 10.855465
sample estimates:
odds ratio
3.472855 print(contingency_table)
ctDNA(-) ctDNA(+)
R0 50 10
R1 17 12table_df <- as.data.frame(contingency_table)
table_df$Total <- ave(table_df$Freq, table_df$Var1, FUN = sum)
table_df$Percentage <- table_df$Freq / table_df$Total
table_df$MiddlePercentage <- table_df$Percentage / 2
ggplot(table_df, aes(x = Var1, y = Percentage, fill = Var2)) +
geom_bar(stat = "identity") +
geom_text(aes(y = MiddlePercentage, label = Freq), position = "stack", color = "black", vjust = 1.5, size = 7) +
theme_minimal() +
labs(title = "ctDNA status - Margins",
x = "Margins",
y = "Patients (%)",
fill = "ctDNA MRD",
caption = paste("Fisher's exact test p-value: ", format.pval(fisher_exact_test$p.value))) +
scale_y_continuous(labels = scales::percent_format()) +
scale_fill_manual(values = c("ctDNA(-)" = "blue", "ctDNA(+)" = "red")) + # define custom colors
theme(axis.text.x = element_text(angle = 0, hjust = 1.5, size = 14), # increase x-axis text size
axis.text.y = element_text(size = 14, color = "black"), # increase y-axis text size
axis.title.x = element_text(size = 14, color = "black"), # increase x-axis label size
axis.title.y = element_text(size = 14, color = "black"), # increase y-axis label size
legend.text = element_text(size = 12, color = "black")) # increase Progression label size
rm(list=ls())
setwd("~/Downloads")
circ_data <- read.csv("ASAN_ClinicalData_GL_082023.csv")
circ_data$ctDNA.MRD <- factor(circ_data$ctDNA.MRD, levels = c("NEGATIVE", "POSITIVE"), labels = c("ctDNA(-)", "ctDNA(+)"))
circ_data$PrimSite <- factor(circ_data$PrimSite, levels = c("pCCA", "dCCA"), labels = c("pCCA", "dCCA"))
contingency_table <- table(circ_data$PrimSite, circ_data$ctDNA.MRD)
chi_square_test <- chisq.test(contingency_table)
print(chi_square_test)
Pearson's Chi-squared test with Yates' continuity correction
data: contingency_table
X-squared = 0, df = 1, p-value = 1fisher_exact_test <- fisher.test(contingency_table)
print(fisher_exact_test)
Fisher's Exact Test for Count Data
data: contingency_table
p-value = 1
alternative hypothesis: true odds ratio is not equal to 1
95 percent confidence interval:
0.3114469 2.6872455
sample estimates:
odds ratio
0.9152081 print(contingency_table)
ctDNA(-) ctDNA(+)
pCCA 32 11
dCCA 35 11table_df <- as.data.frame(contingency_table)
table_df$Total <- ave(table_df$Freq, table_df$Var1, FUN = sum)
table_df$Percentage <- table_df$Freq / table_df$Total
table_df$MiddlePercentage <- table_df$Percentage / 2
ggplot(table_df, aes(x = Var1, y = Percentage, fill = Var2)) +
geom_bar(stat = "identity") +
geom_text(aes(y = MiddlePercentage, label = Freq), position = "stack", color = "black", vjust = 1.5, size = 7) +
theme_minimal() +
labs(title = "ctDNA status - Primary Site",
x = "Primary Site",
y = "Patients (%)",
fill = "ctDNA MRD",
caption = paste("Fisher's exact test p-value: ", format.pval(fisher_exact_test$p.value))) +
scale_y_continuous(labels = scales::percent_format()) +
scale_fill_manual(values = c("ctDNA(-)" = "blue", "ctDNA(+)" = "red")) + # define custom colors
theme(axis.text.x = element_text(angle = 0, hjust = 1.5, size = 14), # increase x-axis text size
axis.text.y = element_text(size = 14, color = "black"), # increase y-axis text size
axis.title.x = element_text(size = 14, color = "black"), # increase x-axis label size
axis.title.y = element_text(size = 14, color = "black"), # increase y-axis label size
legend.text = element_text(size = 12, color = "black")) # increase Progression label size
rm(list=ls())
setwd("~/Downloads")
circ_data <- read.csv("ASAN_ClinicalData_GL_082023.csv")
circ_data$ctDNA.MRD <- factor(circ_data$ctDNA.MRD, levels = c("NEGATIVE", "POSITIVE"), labels = c("ctDNA(-)", "ctDNA(+)"))
circ_data$Stage <- factor(circ_data$Stage, levels = c("II", "III", "IV"), labels = c("II", "III", "IV"))
contingency_table <- table(circ_data$Stage, circ_data$ctDNA.MRD)
chi_square_test <- chisq.test(contingency_table)Warning in chisq.test(contingency_table) :
Chi-squared approximation may be incorrectprint(chi_square_test)
Pearson's Chi-squared test
data: contingency_table
X-squared = 2.68, df = 2, p-value = 0.2619fisher_exact_test <- fisher.test(contingency_table)
print(fisher_exact_test)
Fisher's Exact Test for Count Data
data: contingency_table
p-value = 0.2546
alternative hypothesis: two.sidedprint(contingency_table)
ctDNA(-) ctDNA(+)
II 31 7
III 29 10
IV 7 5table_df <- as.data.frame(contingency_table)
table_df$Total <- ave(table_df$Freq, table_df$Var1, FUN = sum)
table_df$Percentage <- table_df$Freq / table_df$Total
table_df$MiddlePercentage <- table_df$Percentage / 2
ggplot(table_df, aes(x = Var1, y = Percentage, fill = Var2)) +
geom_bar(stat = "identity") +
geom_text(aes(y = MiddlePercentage, label = Freq), position = "stack", color = "black", vjust = 1.5, size = 7) +
theme_minimal() +
labs(title = "ctDNA status - Stage",
x = "Stage",
y = "Patients (%)",
fill = "ctDNA MRD",
caption = paste("Fisher's exact test p-value: ", format.pval(fisher_exact_test$p.value))) +
scale_y_continuous(labels = scales::percent_format()) +
scale_fill_manual(values = c("ctDNA(-)" = "blue", "ctDNA(+)" = "red")) + # define custom colors
theme(axis.text.x = element_text(angle = 0, hjust = 1.5, size = 14), # increase x-axis text size
axis.text.y = element_text(size = 14, color = "black"), # increase y-axis text size
axis.title.x = element_text(size = 14, color = "black"), # increase x-axis label size
axis.title.y = element_text(size = 14, color = "black"), # increase y-axis label size
legend.text = element_text(size = 12, color = "black")) # increase Progression label size
pairwise_fisher <- function(data, factor1, factor2) {
levels <- unique(data[[factor1]])
results <- data.frame(Stage1 = character(), Stage2 = character(), p.value = numeric(), stringsAsFactors = FALSE)
for (i in 1:(length(levels) - 1)) {
for (j in (i + 1):length(levels)) {
subset_data <- data %>% filter(data[[factor1]] %in% c(levels[i], levels[j]))
contingency_table_pairwise <- table(subset_data[[factor1]], subset_data[[factor2]])
fisher_result <- fisher.test(contingency_table_pairwise)
results <- rbind(results, data.frame(Stage1 = levels[i], Stage2 = levels[j], p.value = fisher_result$p.value))
}
}
return(results)
}
# Perform pairwise comparisons
pairwise_results <- pairwise_fisher(circ_data, "Stage", "ctDNA.MRD")
print(pairwise_results)
rm(list=ls())
setwd("~/Downloads")
circ_data <- read.csv("ASAN_ClinicalData_GL_082023.csv")
circ_data$ctDNA.MRD <- factor(circ_data$ctDNA.MRD, levels = c("NEGATIVE", "POSITIVE"), labels = c("ctDNA(-)", "ctDNA(+)"))
circ_data$CA19.MRD <- factor(circ_data$CA19.MRD, levels = c("Normal", "Elevated"), labels = c("Normal", "Elevated"))
contingency_table <- table(circ_data$CA19.MRD, circ_data$ctDNA.MRD)
chi_square_test <- chisq.test(contingency_table)Warning in chisq.test(contingency_table) :
Chi-squared approximation may be incorrectprint(chi_square_test)
Pearson's Chi-squared test with Yates' continuity correction
data: contingency_table
X-squared = 6.1961, df = 1, p-value = 0.0128fisher_exact_test <- fisher.test(contingency_table)
print(fisher_exact_test)
Fisher's Exact Test for Count Data
data: contingency_table
p-value = 0.008766
alternative hypothesis: true odds ratio is not equal to 1
95 percent confidence interval:
1.282588 18.543083
sample estimates:
odds ratio
4.787433 print(contingency_table)
ctDNA(-) ctDNA(+)
Normal 60 14
Elevated 7 8table_df <- as.data.frame(contingency_table)
table_df$Total <- ave(table_df$Freq, table_df$Var1, FUN = sum)
table_df$Percentage <- table_df$Freq / table_df$Total
table_df$MiddlePercentage <- table_df$Percentage / 2
ggplot(table_df, aes(x = Var1, y = Percentage, fill = Var2)) +
geom_bar(stat = "identity") +
geom_text(aes(y = MiddlePercentage, label = Freq), position = "stack", color = "black", vjust = 1.5, size = 7) +
theme_minimal() +
labs(title = "ctDNA status - CA 19-9",
x = "CA 19-9",
y = "Patients (%)",
fill = "ctDNA MRD",
caption = paste("Fisher's exact test p-value: ", format.pval(fisher_exact_test$p.value))) +
scale_y_continuous(labels = scales::percent_format()) +
scale_fill_manual(values = c("ctDNA(-)" = "blue", "ctDNA(+)" = "red")) + # define custom colors
theme(axis.text.x = element_text(angle = 0, hjust = 1.5, size = 14), # increase x-axis text size
axis.text.y = element_text(size = 14, color = "black"), # increase y-axis text size
axis.title.x = element_text(size = 14, color = "black"), # increase x-axis label size
axis.title.y = element_text(size = 14, color = "black"), # increase y-axis label size
legend.text = element_text(size = 12, color = "black")) # increase Progression label size
#Prognostic role of ctDNA at C5D1
rm(list=ls())
setwd("~/Downloads")
circ_data <- read.csv("ASAN_ClinicalData_GL_082023.csv")
circ_data <- circ_data[circ_data$ctDNA.C5D1!="",]
circ_data$RFS.months=circ_data$RFS.months-2
circ_data <- circ_data[circ_data$RFS.months>=0,]
circ_data$RFS.months[circ_data$RFS.months > 60] <- 60
circ_datadf <- as.data.frame(circ_data)
survfit(Surv(time = circ_data$RFS.months, event = circ_data$RFS.Event)~ctDNA.C5D1, data = circ_data)Call: survfit(formula = Surv(time = circ_data$RFS.months, event = circ_data$RFS.Event) ~
ctDNA.C5D1, data = circ_data)
n events median 0.95LCL 0.95UCL
ctDNA.C5D1=NEGATIVE 71 48 15.9 12.92 27.57
ctDNA.C5D1=POSITIVE 17 17 4.8 3.09 7.86event_summary <- circ_data %>%
group_by(ctDNA.C5D1) %>%
summarise(
Total = n(),
Events = sum(RFS.Event),
Fraction = Events / n(),
Percentage = (Events / n()) * 100
)
print(event_summary)surv_object <-Surv(time = circ_data$RFS.months, event = circ_data$RFS.Event)
KM_curve <- survfit(surv_object ~ ctDNA.C5D1, data = circ_data,conf.int=0.95,conf.type="log-log")
ggsurvplot(KM_curve, data = circ_data, pval = FALSE, conf.int = FALSE, risk.table = TRUE, break.time.by=12, palette=c("blue","red"), title="DFS - ctDNA C5D1", ylab= "Disease Free Survival", xlab="Time (Months)", legend.labs=c("ctDNA Negative", "ctDNA Positive"), legend.title="")
summary(KM_curve, times= c(12, 24))Call: survfit(formula = surv_object ~ ctDNA.C5D1, data = circ_data,
conf.int = 0.95, conf.type = "log-log")
ctDNA.C5D1=NEGATIVE
time n.risk n.event survival std.err lower 95% CI upper 95% CI
12 43 27 0.615 0.0581 0.491 0.717
24 30 12 0.441 0.0596 0.323 0.553
ctDNA.C5D1=POSITIVE
time n.risk n.event survival std.err lower 95% CI upper 95% CI
12.00000 1.00000 16.00000 0.05882 0.05707 0.00391 0.23501 circ_data$ctDNA.C5D1 <- factor(circ_data$ctDNA.C5D1, levels=c("NEGATIVE","POSITIVE"))
cox_fit <- coxph(surv_object ~ ctDNA.C5D1, data=circ_data)
ggforest(cox_fit,data = circ_data) 
summary(cox_fit)Call:
coxph(formula = surv_object ~ ctDNA.C5D1, data = circ_data)
n= 88, number of events= 65
coef exp(coef) se(coef) z Pr(>|z|)
ctDNA.C5D1POSITIVE 2.0439 7.7207 0.3238 6.312 2.76e-10 ***
---
Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1
exp(coef) exp(-coef) lower .95 upper .95
ctDNA.C5D1POSITIVE 7.721 0.1295 4.093 14.56
Concordance= 0.638 (se = 0.026 )
Likelihood ratio test= 31.63 on 1 df, p=2e-08
Wald test = 39.84 on 1 df, p=3e-10
Score (logrank) test = 53.01 on 1 df, p=3e-13cox_fit_summary <- summary(cox_fit)
# Extract values for HR, 95% CI, and p-value
HR <- cox_fit_summary$coefficients[2]
lower_CI <- cox_fit_summary$conf.int[3]
upper_CI <- cox_fit_summary$conf.int[4]
p_value <- cox_fit_summary$coefficients[5]
label_text <- paste0("HR = ", round(HR, 2), " (", round(lower_CI, 2), "-", round(upper_CI, 2), "); p = ", round(p_value, 3))
print(label_text)[1] "HR = 7.72 (4.09-14.56); p = 0"#Multivariate regression model for DFS with ctDNA and CA 19-9
rm(list=ls())
setwd("~/Downloads")
circ_data <- read.csv("ASAN_ClinicalData_GL_082023.csv")
circ_data <- subset(circ_data, !is.na(ctDNA.C5D1))
circ_data$RFS.months=circ_data$RFS.months-2
circ_data <- circ_data[circ_data$RFS.months>=0,]
circ_data$RFS.months[circ_data$RFS.months > 60] <- 60
circ_datadf <- as.data.frame(circ_data)
circ_datadf$Sex <- factor(circ_datadf$Sex, levels = c("Female", "Male"), labels = c("Female", "Male"))
circ_datadf$PrimSite <- factor(circ_datadf$PrimSite, levels = c("pCCA", "dCCA"), labels = c("pCCA", "dCCA"))
circ_datadf$Chemo <- factor(circ_datadf$Chemo, levels = c("CAP", "GemCis"), labels = c("CAP", "GemCis"))
circ_datadf$ResMarg <- factor(circ_datadf$ResMarg, levels = c("R0", "R1"))
circ_datadf$Stage <- factor(circ_datadf$Stage, levels = c("II", "III", "IV"), labels = c("II", "III", "IV"))
circ_datadf$TP53 <- factor(circ_datadf$TP53, levels = c("WT", "Mut"))
circ_datadf$CA19.C5D1 <- factor(circ_datadf$CA19.C5D1, levels = c("Normal", "Elevated"))
circ_datadf$ctDNA.C5D1 <- factor(circ_datadf$ctDNA.C5D1, levels = c("NEGATIVE", "POSITIVE"), labels = c("Negative", "Positive"))
surv_object<-Surv(time = circ_datadf$RFS.months, event = circ_datadf$RFS.Event)
cox_fit <- coxph(surv_object ~ Sex + Age + PrimSite + Stage + Chemo + ResMarg + CA19.C5D1 + ctDNA.C5D1, data=circ_datadf)
ggforest(cox_fit, data = circ_datadf, main = "Multivariate Regression Model for DFS - Landmark analysis", refLabel = "Reference Group")
test.ph <- cox.zph(cox_fit)#Univariate regression model for factors used at the C5D1 MVA
rm(list=ls())
setwd("~/Downloads")
circ_data <- read.csv("ASAN_ClinicalData_GL_082023.csv")
circ_data <- subset(circ_data, !is.na(ctDNA.C5D1))
circ_data$RFS.months=circ_data$RFS.months-2
circ_data <- circ_data[circ_data$RFS.months>=0,]
circ_data$RFS.months[circ_data$RFS.months > 60] <- 60
surv_object <-Surv(time = circ_data$RFS.months, event = circ_data$RFS.Event)
circ_data$Sex <- factor(circ_data$Sex, levels = c("Female", "Male"), labels = c("Female", "Male")) #univariate for gender
cox_fit <- coxph(surv_object ~ Sex, data=circ_data)
summary(cox_fit)Call:
coxph(formula = surv_object ~ Sex, data = circ_data)
n= 89, number of events= 65
coef exp(coef) se(coef) z Pr(>|z|)
SexMale 0.1594 1.1728 0.2689 0.593 0.553
exp(coef) exp(-coef) lower .95 upper .95
SexMale 1.173 0.8527 0.6923 1.987
Concordance= 0.515 (se = 0.031 )
Likelihood ratio test= 0.36 on 1 df, p=0.5
Wald test = 0.35 on 1 df, p=0.6
Score (logrank) test = 0.35 on 1 df, p=0.6cox_fit_summary <- summary(cox_fit)
HR <- cox_fit_summary$coefficients[2]
lower_CI <- cox_fit_summary$conf.int[3]
upper_CI <- cox_fit_summary$conf.int[4]
p_value <- cox_fit_summary$coefficients[5]
label_text <- paste0("HR = ", round(HR, 2), " (", round(lower_CI, 2), "-", round(upper_CI, 2), "); p = ", round(p_value, 3))
print(label_text)[1] "HR = 1.17 (0.69-1.99); p = 0.553"rm(list=ls())
setwd("~/Downloads")
circ_data <- read.csv("ASAN_ClinicalData_GL_082023.csv")
circ_data <- subset(circ_data, !is.na(ctDNA.C5D1))
circ_data$RFS.months=circ_data$RFS.months-2
circ_data <- circ_data[circ_data$RFS.months>=0,]
circ_data$RFS.months[circ_data$RFS.months > 60] <- 60
surv_object <-Surv(time = circ_data$RFS.months, event = circ_data$RFS.Event)
cox_fit <- coxph(surv_object ~ Age, data=circ_data) #univariate for age
summary(cox_fit)Call:
coxph(formula = surv_object ~ Age, data = circ_data)
n= 89, number of events= 65
coef exp(coef) se(coef) z Pr(>|z|)
Age 0.002393 1.002396 0.019228 0.124 0.901
exp(coef) exp(-coef) lower .95 upper .95
Age 1.002 0.9976 0.9653 1.041
Concordance= 0.493 (se = 0.041 )
Likelihood ratio test= 0.02 on 1 df, p=0.9
Wald test = 0.02 on 1 df, p=0.9
Score (logrank) test = 0.02 on 1 df, p=0.9cox_fit_summary <- summary(cox_fit)
HR <- cox_fit_summary$coefficients[2]
lower_CI <- cox_fit_summary$conf.int[3]
upper_CI <- cox_fit_summary$conf.int[4]
p_value <- cox_fit_summary$coefficients[5]
label_text <- paste0("HR = ", round(HR, 2), " (", round(lower_CI, 2), "-", round(upper_CI, 2), "); p = ", round(p_value, 3))
print(label_text)[1] "HR = 1 (0.97-1.04); p = 0.901"rm(list=ls())
setwd("~/Downloads")
circ_data <- read.csv("ASAN_ClinicalData_GL_082023.csv")
circ_data <- subset(circ_data, !is.na(ctDNA.C5D1))
circ_data$RFS.months=circ_data$RFS.months-2
circ_data <- circ_data[circ_data$RFS.months>=0,]
circ_data$RFS.months[circ_data$RFS.months > 60] <- 60
surv_object <-Surv(time = circ_data$RFS.months, event = circ_data$RFS.Event)
circ_data$PrimSite <- factor(circ_data$PrimSite, levels = c("pCCA", "dCCA"), labels = c("pCCA", "dCCA")) #univariate for Primary Site
cox_fit <- coxph(surv_object ~ PrimSite, data=circ_data)
summary(cox_fit)Call:
coxph(formula = surv_object ~ PrimSite, data = circ_data)
n= 89, number of events= 65
coef exp(coef) se(coef) z Pr(>|z|)
PrimSitedCCA -0.4558 0.6340 0.2520 -1.809 0.0705 .
---
Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1
exp(coef) exp(-coef) lower .95 upper .95
PrimSitedCCA 0.634 1.577 0.3869 1.039
Concordance= 0.535 (se = 0.034 )
Likelihood ratio test= 3.31 on 1 df, p=0.07
Wald test = 3.27 on 1 df, p=0.07
Score (logrank) test = 3.33 on 1 df, p=0.07cox_fit_summary <- summary(cox_fit)
HR <- cox_fit_summary$coefficients[2]
lower_CI <- cox_fit_summary$conf.int[3]
upper_CI <- cox_fit_summary$conf.int[4]
p_value <- cox_fit_summary$coefficients[5]
label_text <- paste0("HR = ", round(HR, 2), " (", round(lower_CI, 2), "-", round(upper_CI, 2), "); p = ", round(p_value, 3))
print(label_text)[1] "HR = 0.63 (0.39-1.04); p = 0.071"rm(list=ls())
setwd("~/Downloads")
circ_data <- read.csv("ASAN_ClinicalData_GL_082023.csv")
circ_data <- subset(circ_data, !is.na(ctDNA.C5D1))
circ_data$RFS.months=circ_data$RFS.months-2
circ_data <- circ_data[circ_data$RFS.months>=0,]
circ_data$RFS.months[circ_data$RFS.months > 60] <- 60
surv_object <-Surv(time = circ_data$RFS.months, event = circ_data$RFS.Event)
KM_curve <- survfit(surv_object ~ Stage, data = circ_data,conf.int=0.95,conf.type="log-log")
ggsurvplot(KM_curve, data = circ_data, pval = TRUE, conf.int = FALSE, risk.table = TRUE, break.time.by=3, palette=c("blue","green","red"), title="DFS - ctDNA C5D1 - Stage", ylab= "Disease Free Survival", xlab="Time (Months)", legend.labs=c("II", "III", "IV"), legend.title="")
circ_data$Stage <- factor(circ_data$Stage, levels = c("II", "III", "IV"), labels = c("II", "III", "IV")) #univariate for Stage
cox_fit <- coxph(surv_object ~ Stage, data=circ_data)
ggforest(cox_fit,data = circ_data) 
summary(cox_fit)Call:
coxph(formula = surv_object ~ Stage, data = circ_data)
n= 89, number of events= 65
coef exp(coef) se(coef) z Pr(>|z|)
StageIII 0.2297 1.2583 0.2777 0.827 0.40805
StageIV 1.1237 3.0761 0.3631 3.095 0.00197 **
---
Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1
exp(coef) exp(-coef) lower .95 upper .95
StageIII 1.258 0.7947 0.7301 2.168
StageIV 3.076 0.3251 1.5099 6.267
Concordance= 0.565 (se = 0.037 )
Likelihood ratio test= 8.34 on 2 df, p=0.02
Wald test = 9.93 on 2 df, p=0.007
Score (logrank) test = 10.77 on 2 df, p=0.005rm(list=ls())
setwd("~/Downloads")
circ_data <- read.csv("ASAN_ClinicalData_GL_082023.csv")
circ_data <- subset(circ_data, !is.na(ctDNA.C5D1))
circ_data$RFS.months=circ_data$RFS.months-2
circ_data <- circ_data[circ_data$RFS.months>=0,]
circ_data$RFS.months[circ_data$RFS.months > 60] <- 60
surv_object <-Surv(time = circ_data$RFS.months, event = circ_data$RFS.Event)
circ_data$Chemo <- factor(circ_data$Chemo, levels = c("CAP", "GemCis"), labels = c("CAP", "GemCis")) #univariate for Chemotherapy
cox_fit <- coxph(surv_object ~ Chemo, data=circ_data)
summary(cox_fit)Call:
coxph(formula = surv_object ~ Chemo, data = circ_data)
n= 89, number of events= 65
coef exp(coef) se(coef) z Pr(>|z|)
ChemoGemCis -0.02437 0.97592 0.24846 -0.098 0.922
exp(coef) exp(-coef) lower .95 upper .95
ChemoGemCis 0.9759 1.025 0.5997 1.588
Concordance= 0.507 (se = 0.034 )
Likelihood ratio test= 0.01 on 1 df, p=0.9
Wald test = 0.01 on 1 df, p=0.9
Score (logrank) test = 0.01 on 1 df, p=0.9cox_fit_summary <- summary(cox_fit)
HR <- cox_fit_summary$coefficients[2]
lower_CI <- cox_fit_summary$conf.int[3]
upper_CI <- cox_fit_summary$conf.int[4]
p_value <- cox_fit_summary$coefficients[5]
label_text <- paste0("HR = ", round(HR, 2), " (", round(lower_CI, 2), "-", round(upper_CI, 2), "); p = ", round(p_value, 3))
print(label_text)[1] "HR = 0.98 (0.6-1.59); p = 0.922"rm(list=ls())
setwd("~/Downloads")
circ_data <- read.csv("ASAN_ClinicalData_GL_082023.csv")
circ_data <- subset(circ_data, !is.na(ctDNA.C5D1))
circ_data$RFS.months=circ_data$RFS.months-2
circ_data <- circ_data[circ_data$RFS.months>=0,]
circ_data$RFS.months[circ_data$RFS.months > 60] <- 60
surv_object <-Surv(time = circ_data$RFS.months, event = circ_data$RFS.Event)
circ_data$ResMarg <- factor(circ_data$ResMarg, levels = c("R0", "R1")) #univariate for Resection margin
cox_fit <- coxph(surv_object ~ ResMarg, data=circ_data)
summary(cox_fit)Call:
coxph(formula = surv_object ~ ResMarg, data = circ_data)
n= 89, number of events= 65
coef exp(coef) se(coef) z Pr(>|z|)
ResMargR1 0.3415 1.4071 0.2617 1.305 0.192
exp(coef) exp(-coef) lower .95 upper .95
ResMargR1 1.407 0.7107 0.8425 2.35
Concordance= 0.534 (se = 0.03 )
Likelihood ratio test= 1.64 on 1 df, p=0.2
Wald test = 1.7 on 1 df, p=0.2
Score (logrank) test = 1.72 on 1 df, p=0.2cox_fit_summary <- summary(cox_fit)
HR <- cox_fit_summary$coefficients[2]
lower_CI <- cox_fit_summary$conf.int[3]
upper_CI <- cox_fit_summary$conf.int[4]
p_value <- cox_fit_summary$coefficients[5]
label_text <- paste0("HR = ", round(HR, 2), " (", round(lower_CI, 2), "-", round(upper_CI, 2), "); p = ", round(p_value, 3))
print(label_text)[1] "HR = 1.41 (0.84-2.35); p = 0.192"rm(list=ls())
setwd("~/Downloads")
circ_data <- read.csv("ASAN_ClinicalData_GL_082023.csv")
circ_data <- subset(circ_data, !is.na(ctDNA.C5D1))
circ_data$RFS.months=circ_data$RFS.months-2
circ_data <- circ_data[circ_data$RFS.months>=0,]
circ_data$RFS.months[circ_data$RFS.months > 60] <- 60
surv_object <-Surv(time = circ_data$RFS.months, event = circ_data$RFS.Event)
circ_data$CA19.C5D1 <- factor(circ_data$CA19.C5D1, levels = c("Normal", "Elevated")) #univariate for CA 19-9 C5D1
cox_fit <- coxph(surv_object ~ CA19.C5D1, data=circ_data)
summary(cox_fit)Call:
coxph(formula = surv_object ~ CA19.C5D1, data = circ_data)
n= 80, number of events= 56
(9 observations deleted due to missingness)
coef exp(coef) se(coef) z Pr(>|z|)
CA19.C5D1Elevated 0.2218 1.2483 0.3264 0.679 0.497
exp(coef) exp(-coef) lower .95 upper .95
CA19.C5D1Elevated 1.248 0.8011 0.6584 2.367
Concordance= 0.535 (se = 0.032 )
Likelihood ratio test= 0.44 on 1 df, p=0.5
Wald test = 0.46 on 1 df, p=0.5
Score (logrank) test = 0.46 on 1 df, p=0.5cox_fit_summary <- summary(cox_fit)
HR <- cox_fit_summary$coefficients[2]
lower_CI <- cox_fit_summary$conf.int[3]
upper_CI <- cox_fit_summary$conf.int[4]
p_value <- cox_fit_summary$coefficients[5]
label_text <- paste0("HR = ", round(HR, 2), " (", round(lower_CI, 2), "-", round(upper_CI, 2), "); p = ", round(p_value, 3))
print(label_text)[1] "HR = 1.25 (0.66-2.37); p = 0.497"rm(list=ls())
setwd("~/Downloads")
circ_data <- read.csv("ASAN_ClinicalData_GL_082023.csv")
circ_data <- subset(circ_data, !is.na(ctDNA.C5D1))
circ_data$RFS.months=circ_data$RFS.months-2
circ_data <- circ_data[circ_data$RFS.months>=0,]
circ_data$RFS.months[circ_data$RFS.months > 60] <- 60
surv_object <-Surv(time = circ_data$RFS.months, event = circ_data$RFS.Event)
circ_data$ctDNA.C5D1 <- factor(circ_data$ctDNA.C5D1, levels = c("NEGATIVE", "POSITIVE"), labels = c("Negative", "Positive")) #univariate for ctDNA C5D1
cox_fit <- coxph(surv_object ~ ctDNA.C5D1, data=circ_data)
summary(cox_fit)Call:
coxph(formula = surv_object ~ ctDNA.C5D1, data = circ_data)
n= 88, number of events= 65
(1 observation deleted due to missingness)
coef exp(coef) se(coef) z Pr(>|z|)
ctDNA.C5D1Positive 2.0439 7.7207 0.3238 6.312 2.76e-10 ***
---
Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1
exp(coef) exp(-coef) lower .95 upper .95
ctDNA.C5D1Positive 7.721 0.1295 4.093 14.56
Concordance= 0.638 (se = 0.026 )
Likelihood ratio test= 31.63 on 1 df, p=2e-08
Wald test = 39.84 on 1 df, p=3e-10
Score (logrank) test = 53.01 on 1 df, p=3e-13cox_fit_summary <- summary(cox_fit)
HR <- cox_fit_summary$coefficients[2]
lower_CI <- cox_fit_summary$conf.int[3]
upper_CI <- cox_fit_summary$conf.int[4]
p_value <- cox_fit_summary$coefficients[5]
label_text <- paste0("HR = ", round(HR, 2), " (", round(lower_CI, 2), "-", round(upper_CI, 2), "); p = ", round(p_value, 3))
print(label_text)[1] "HR = 7.72 (4.09-14.56); p = 0"#OS by ctDNA at C5D1
rm(list=ls())
setwd("~/Downloads")
circ_data <- read.csv("ASAN_ClinicalData_GL_082023.csv")
circ_data <- circ_data[circ_data$ctDNA.C5D1!="",]
circ_data$OS.months=circ_data$OS.months-2
circ_data <- circ_data[circ_data$OS.months>=0,]
circ_datadf <- as.data.frame(circ_data)
survfit(Surv(time = circ_data$OS.months, event = circ_data$OS.Event)~ctDNA.C5D1, data = circ_data)Call: survfit(formula = Surv(time = circ_data$OS.months, event = circ_data$OS.Event) ~
ctDNA.C5D1, data = circ_data)
n events median 0.95LCL 0.95UCL
ctDNA.C5D1=NEGATIVE 71 39 44.8 31.7 NA
ctDNA.C5D1=POSITIVE 17 17 18.4 13.1 35.3event_summary <- circ_data %>%
group_by(ctDNA.C5D1) %>%
summarise(
Total = n(),
Events = sum(OS.Event),
Fraction = Events / n(),
Percentage = (Events / n()) * 100
)
print(event_summary)surv_object <-Surv(time = circ_data$OS.months, event = circ_data$OS.Event)
KM_curve <- survfit(surv_object ~ ctDNA.C5D1, data = circ_data,conf.int=0.95,conf.type="log-log")
ggsurvplot(KM_curve, data = circ_data, pval = FALSE, conf.int = FALSE, risk.table = TRUE, break.time.by=12, palette=c("blue","red"), title="OS - ctDNA C5D1", ylab= "Overall Survival", xlab="Time (Months)", legend.labs=c("ctDNA Negative", "ctDNA Positive"), legend.title="")
summary(KM_curve, times= c(12, 24))Call: survfit(formula = surv_object ~ ctDNA.C5D1, data = circ_data,
conf.int = 0.95, conf.type = "log-log")
ctDNA.C5D1=NEGATIVE
time n.risk n.event survival std.err lower 95% CI upper 95% CI
12 69 2 0.972 0.0196 0.892 0.993
24 56 13 0.789 0.0484 0.674 0.867
ctDNA.C5D1=POSITIVE
time n.risk n.event survival std.err lower 95% CI upper 95% CI
12 12 5 0.706 0.111 0.431 0.866
24 7 5 0.412 0.119 0.186 0.626circ_data$ctDNA.C5D1 <- factor(circ_data$ctDNA.C5D1, levels=c("NEGATIVE","POSITIVE"))
cox_fit <- coxph(surv_object ~ ctDNA.C5D1, data=circ_data)
ggforest(cox_fit,data = circ_data) 
summary(cox_fit)Call:
coxph(formula = surv_object ~ ctDNA.C5D1, data = circ_data)
n= 88, number of events= 56
coef exp(coef) se(coef) z Pr(>|z|)
ctDNA.C5D1POSITIVE 1.2634 3.5375 0.2983 4.235 2.29e-05 ***
---
Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1
exp(coef) exp(-coef) lower .95 upper .95
ctDNA.C5D1POSITIVE 3.537 0.2827 1.971 6.348
Concordance= 0.603 (se = 0.029 )
Likelihood ratio test= 14.98 on 1 df, p=1e-04
Wald test = 17.93 on 1 df, p=2e-05
Score (logrank) test = 20.38 on 1 df, p=6e-06cox_fit_summary <- summary(cox_fit)
# Extract values for HR, 95% CI, and p-value
HR <- cox_fit_summary$coefficients[2]
lower_CI <- cox_fit_summary$conf.int[3]
upper_CI <- cox_fit_summary$conf.int[4]
p_value <- cox_fit_summary$coefficients[5]
label_text <- paste0("HR = ", round(HR, 2), " (", round(lower_CI, 2), "-", round(upper_CI, 2), "); p = ", round(p_value, 3))
print(label_text)[1] "HR = 3.54 (1.97-6.35); p = 0"#Prognostic role of ctDNA at C8D1
rm(list=ls())
setwd("~/Downloads")
circ_data <- read.csv("ASAN_ClinicalData_GL_082023.csv")
circ_data <- circ_data[circ_data$ctDNA.C8D1!="",]
circ_data$RFS.months=circ_data$RFS.months-2
circ_data <- circ_data[circ_data$RFS.months>=0,]
circ_data$RFS.months[circ_data$RFS.months > 60] <- 60
circ_datadf <- as.data.frame(circ_data)
survfit(Surv(time = circ_data$RFS.months, event = circ_data$RFS.Event)~ctDNA.C8D1, data = circ_data)Call: survfit(formula = Surv(time = circ_data$RFS.months, event = circ_data$RFS.Event) ~
ctDNA.C8D1, data = circ_data)
n events median 0.95LCL 0.95UCL
ctDNA.C8D1=NEGATIVE 62 40 17.02 13.84 33.4
ctDNA.C8D1=POSITIVE 15 15 7.13 5.36 15.2event_summary <- circ_data %>%
group_by(ctDNA.C8D1) %>%
summarise(
Total = n(),
Events = sum(RFS.Event),
Fraction = Events / n(),
Percentage = (Events / n()) * 100
)
print(event_summary)surv_object <-Surv(time = circ_data$RFS.months, event = circ_data$RFS.Event)
KM_curve <- survfit(surv_object ~ ctDNA.C8D1, data = circ_data,conf.int=0.95,conf.type="log-log")
ggsurvplot(KM_curve, data = circ_data, pval = FALSE, conf.int = FALSE, risk.table = TRUE, break.time.by=12, palette=c("blue","red"), title="DFS - ctDNA C8D1", ylab= "Disease Free Survival", xlab="Time (Months)", legend.labs=c("ctDNA Negative", "ctDNA Positive"), legend.title="")
summary(KM_curve, times= c(12, 24))Call: survfit(formula = surv_object ~ ctDNA.C8D1, data = circ_data,
conf.int = 0.95, conf.type = "log-log")
ctDNA.C8D1=NEGATIVE
time n.risk n.event survival std.err lower 95% CI upper 95% CI
12 40 22 0.645 0.0608 0.513 0.750
24 29 10 0.481 0.0637 0.353 0.599
ctDNA.C8D1=POSITIVE
time n.risk n.event survival std.err lower 95% CI upper 95% CI
12.0000 3.0000 12.0000 0.2000 0.1033 0.0489 0.4239 circ_data$ctDNA.C8D1 <- factor(circ_data$ctDNA.C8D1, levels=c("NEGATIVE","POSITIVE"))
cox_fit <- coxph(surv_object ~ ctDNA.C8D1, data=circ_data)
ggforest(cox_fit,data = circ_data) 
summary(cox_fit)Call:
coxph(formula = surv_object ~ ctDNA.C8D1, data = circ_data)
n= 77, number of events= 55
coef exp(coef) se(coef) z Pr(>|z|)
ctDNA.C8D1POSITIVE 1.6561 5.2389 0.3281 5.047 4.49e-07 ***
---
Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1
exp(coef) exp(-coef) lower .95 upper .95
ctDNA.C8D1POSITIVE 5.239 0.1909 2.754 9.967
Concordance= 0.624 (se = 0.028 )
Likelihood ratio test= 20.53 on 1 df, p=6e-06
Wald test = 25.47 on 1 df, p=4e-07
Score (logrank) test = 31.38 on 1 df, p=2e-08cox_fit_summary <- summary(cox_fit)
# Extract values for HR, 95% CI, and p-value
HR <- cox_fit_summary$coefficients[2]
lower_CI <- cox_fit_summary$conf.int[3]
upper_CI <- cox_fit_summary$conf.int[4]
p_value <- cox_fit_summary$coefficients[5]
label_text <- paste0("HR = ", round(HR, 2), " (", round(lower_CI, 2), "-", round(upper_CI, 2), "); p = ", round(p_value, 3))
print(label_text)[1] "HR = 5.24 (2.75-9.97); p = 0"#OS by ctDNA at C8D1
rm(list=ls())
setwd("~/Downloads")
circ_data <- read.csv("ASAN_ClinicalData_GL_082023.csv")
circ_data <- circ_data[circ_data$ctDNA.C8D1!="",]
circ_data$OS.months=circ_data$OS.months-2
circ_data <- circ_data[circ_data$OS.months>=0,]
circ_datadf <- as.data.frame(circ_data)
survfit(Surv(time = circ_data$OS.months, event = circ_data$OS.Event)~ctDNA.C8D1, data = circ_data)Call: survfit(formula = Surv(time = circ_data$OS.months, event = circ_data$OS.Event) ~
ctDNA.C8D1, data = circ_data)
n events median 0.95LCL 0.95UCL
ctDNA.C8D1=NEGATIVE 62 32 48.4 30.1 NA
ctDNA.C8D1=POSITIVE 15 15 29.8 17.5 41.8event_summary <- circ_data %>%
group_by(ctDNA.C8D1) %>%
summarise(
Total = n(),
Events = sum(OS.Event),
Fraction = Events / n(),
Percentage = (Events / n()) * 100
)
print(event_summary)surv_object <-Surv(time = circ_data$OS.months, event = circ_data$OS.Event)
KM_curve <- survfit(surv_object ~ ctDNA.C8D1, data = circ_data,conf.int=0.95,conf.type="log-log")
ggsurvplot(KM_curve, data = circ_data, pval = FALSE, conf.int = FALSE, risk.table = TRUE, break.time.by=12, palette=c("blue","red"), title="OS - ctDNA C8D1", ylab= "Overall Survival", xlab="Time (Months)", legend.labs=c("ctDNA Negative", "ctDNA Positive"), legend.title="")
summary(KM_curve, times= c(12, 24))Call: survfit(formula = surv_object ~ ctDNA.C8D1, data = circ_data,
conf.int = 0.95, conf.type = "log-log")
ctDNA.C8D1=NEGATIVE
time n.risk n.event survival std.err lower 95% CI upper 95% CI
12 60 2 0.968 0.0224 0.877 0.992
24 48 12 0.774 0.0531 0.649 0.860
ctDNA.C8D1=POSITIVE
time n.risk n.event survival std.err lower 95% CI upper 95% CI
12 13 2 0.867 0.0878 0.564 0.965
24 8 5 0.533 0.1288 0.263 0.744circ_data$ctDNA.C8D1 <- factor(circ_data$ctDNA.C8D1, levels=c("NEGATIVE","POSITIVE"))
cox_fit <- coxph(surv_object ~ ctDNA.C8D1, data=circ_data)
ggforest(cox_fit,data = circ_data) 
summary(cox_fit)Call:
coxph(formula = surv_object ~ ctDNA.C8D1, data = circ_data)
n= 77, number of events= 47
coef exp(coef) se(coef) z Pr(>|z|)
ctDNA.C8D1POSITIVE 1.0300 2.8011 0.3192 3.227 0.00125 **
---
Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1
exp(coef) exp(-coef) lower .95 upper .95
ctDNA.C8D1POSITIVE 2.801 0.357 1.499 5.236
Concordance= 0.574 (se = 0.031 )
Likelihood ratio test= 9.08 on 1 df, p=0.003
Wald test = 10.42 on 1 df, p=0.001
Score (logrank) test = 11.35 on 1 df, p=8e-04cox_fit_summary <- summary(cox_fit)
# Extract values for HR, 95% CI, and p-value
HR <- cox_fit_summary$coefficients[2]
lower_CI <- cox_fit_summary$conf.int[3]
upper_CI <- cox_fit_summary$conf.int[4]
p_value <- cox_fit_summary$coefficients[5]
label_text <- paste0("HR = ", round(HR, 2), " (", round(lower_CI, 2), "-", round(upper_CI, 2), "); p = ", round(p_value, 3))
print(label_text)[1] "HR = 2.8 (1.5-5.24); p = 0.001"#Prognostic role of ctDNA anytime post-surgery
rm(list=ls())
setwd("~/Downloads")
circ_data <- read.csv("ASAN_ClinicalData_GL_082023.csv")
circ_data <- circ_data[circ_data$ctDNA.anytime!="",]
circ_data$RFS.months=circ_data$RFS.months-2
circ_data <- circ_data[circ_data$RFS.months>=0,]
circ_data$RFS.months[circ_data$RFS.months > 60] <- 60
circ_datadf <- as.data.frame(circ_data)
survfit(Surv(time = circ_data$RFS.months, event = circ_data$RFS.Event)~ctDNA.anytime, data = circ_data)Call: survfit(formula = Surv(time = circ_data$RFS.months, event = circ_data$RFS.Event) ~
ctDNA.anytime, data = circ_data)
n events median 0.95LCL 0.95UCL
ctDNA.anytime=NEGATIVE 42 20 38.71 19.06 NA
ctDNA.anytime=POSITIVE 47 45 8.05 7.56 12event_summary <- circ_data %>%
group_by(ctDNA.anytime) %>%
summarise(
Total = n(),
Events = sum(RFS.Event),
Fraction = Events / n(),
Percentage = (Events / n()) * 100
)
print(event_summary)surv_object <-Surv(time = circ_data$RFS.months, event = circ_data$RFS.Event)
KM_curve <- survfit(surv_object ~ ctDNA.anytime, data = circ_data,conf.int=0.95,conf.type="log-log")
ggsurvplot(KM_curve, data = circ_data, pval = FALSE, conf.int = FALSE, risk.table = TRUE, break.time.by=12, palette=c("blue","red"), title="DFS - ctDNA anytime post-surgery", ylab= "Disease Free Survival", xlab="Time (Months)", legend.labs=c("ctDNA Negative", "ctDNA Positive"), legend.title="")
summary(KM_curve, times= c(12, 24))Call: survfit(formula = surv_object ~ ctDNA.anytime, data = circ_data,
conf.int = 0.95, conf.type = "log-log")
ctDNA.anytime=NEGATIVE
time n.risk n.event survival std.err lower 95% CI upper 95% CI
12 30 11 0.734 0.0689 0.571 0.843
24 24 5 0.609 0.0766 0.442 0.739
ctDNA.anytime=POSITIVE
time n.risk n.event survival std.err lower 95% CI upper 95% CI
12 15 32 0.319 0.0680 0.1928 0.453
24 7 8 0.149 0.0519 0.0655 0.264circ_data$ctDNA.anytime <- factor(circ_data$ctDNA.anytime, levels=c("NEGATIVE","POSITIVE"))
cox_fit <- coxph(surv_object ~ ctDNA.anytime, data=circ_data)
ggforest(cox_fit,data = circ_data) 
summary(cox_fit)Call:
coxph(formula = surv_object ~ ctDNA.anytime, data = circ_data)
n= 89, number of events= 65
coef exp(coef) se(coef) z Pr(>|z|)
ctDNA.anytimePOSITIVE 1.3374 3.8090 0.2757 4.851 1.23e-06 ***
---
Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1
exp(coef) exp(-coef) lower .95 upper .95
ctDNA.anytimePOSITIVE 3.809 0.2625 2.219 6.538
Concordance= 0.649 (se = 0.03 )
Likelihood ratio test= 26 on 1 df, p=3e-07
Wald test = 23.54 on 1 df, p=1e-06
Score (logrank) test = 26.68 on 1 df, p=2e-07cox_fit_summary <- summary(cox_fit)
# Extract values for HR, 95% CI, and p-value
HR <- cox_fit_summary$coefficients[2]
lower_CI <- cox_fit_summary$conf.int[3]
upper_CI <- cox_fit_summary$conf.int[4]
p_value <- cox_fit_summary$coefficients[5]
label_text <- paste0("HR = ", round(HR, 2), " (", round(lower_CI, 2), "-", round(upper_CI, 2), "); p = ", round(p_value, 3))
print(label_text)[1] "HR = 3.81 (2.22-6.54); p = 0"#OS by ctDNA anytime post-surgery
rm(list=ls())
setwd("~/Downloads")
circ_data <- read.csv("ASAN_ClinicalData_GL_082023.csv")
circ_data <- circ_data[circ_data$ctDNA.anytime!="",]
circ_data$OS.months=circ_data$OS.months-2
circ_data <- circ_data[circ_data$OS.months>=0,]
circ_datadf <- as.data.frame(circ_data)
survfit(Surv(time = circ_data$OS.months, event = circ_data$OS.Event)~ctDNA.anytime, data = circ_data)Call: survfit(formula = Surv(time = circ_data$OS.months, event = circ_data$OS.Event) ~
ctDNA.anytime, data = circ_data)
n events median 0.95LCL 0.95UCL
ctDNA.anytime=NEGATIVE 42 15 NA 48.4 NA
ctDNA.anytime=POSITIVE 47 41 27.6 21.3 35.3event_summary <- circ_data %>%
group_by(ctDNA.anytime) %>%
summarise(
Total = n(),
Events = sum(OS.Event),
Fraction = Events / n(),
Percentage = (Events / n()) * 100
)
print(event_summary)surv_object <-Surv(time = circ_data$OS.months, event = circ_data$OS.Event)
KM_curve <- survfit(surv_object ~ ctDNA.anytime, data = circ_data,conf.int=0.95,conf.type="log-log")
ggsurvplot(KM_curve, data = circ_data, pval = FALSE, conf.int = FALSE, risk.table = TRUE, break.time.by=12, palette=c("blue","red"), title="OS - ctDNA anytime post-surgery", ylab= "Overall Survival", xlab="Time (Months)", legend.labs=c("ctDNA Negative", "ctDNA Positive"), legend.title="")
summary(KM_curve, times= c(12, 24))Call: survfit(formula = surv_object ~ ctDNA.anytime, data = circ_data,
conf.int = 0.95, conf.type = "log-log")
ctDNA.anytime=NEGATIVE
time n.risk n.event survival std.err lower 95% CI upper 95% CI
12 41 1 0.976 0.0235 0.843 0.997
24 38 3 0.905 0.0453 0.766 0.963
ctDNA.anytime=POSITIVE
time n.risk n.event survival std.err lower 95% CI upper 95% CI
12 41 6 0.872 0.0487 0.738 0.941
24 26 15 0.553 0.0725 0.401 0.681circ_data$ctDNA.anytime <- factor(circ_data$ctDNA.anytime, levels=c("NEGATIVE","POSITIVE"))
cox_fit <- coxph(surv_object ~ ctDNA.anytime, data=circ_data)
ggforest(cox_fit,data = circ_data) 
summary(cox_fit)Call:
coxph(formula = surv_object ~ ctDNA.anytime, data = circ_data)
n= 89, number of events= 56
coef exp(coef) se(coef) z Pr(>|z|)
ctDNA.anytimePOSITIVE 1.3272 3.7706 0.3039 4.367 1.26e-05 ***
---
Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1
exp(coef) exp(-coef) lower .95 upper .95
ctDNA.anytimePOSITIVE 3.771 0.2652 2.078 6.841
Concordance= 0.648 (se = 0.033 )
Likelihood ratio test= 21.97 on 1 df, p=3e-06
Wald test = 19.07 on 1 df, p=1e-05
Score (logrank) test = 21.89 on 1 df, p=3e-06cox_fit_summary <- summary(cox_fit)
# Extract values for HR, 95% CI, and p-value
HR <- cox_fit_summary$coefficients[2]
lower_CI <- cox_fit_summary$conf.int[3]
upper_CI <- cox_fit_summary$conf.int[4]
p_value <- cox_fit_summary$coefficients[5]
label_text <- paste0("HR = ", round(HR, 2), " (", round(lower_CI, 2), "-", round(upper_CI, 2), "); p = ", round(p_value, 3))
print(label_text)[1] "HR = 3.77 (2.08-6.84); p = 0"#Prognostic role of ctDNA Dynamics
rm(list=ls())
setwd("~/Downloads")
circ_data <- read.csv("ASAN_ClinicalData_GL_082023.csv")
circ_data$RFS.months=circ_data$RFS.months-2
circ_data <- circ_data[circ_data$RFS.months>=0,]
circ_data$RFS.months[circ_data$RFS.months > 48] <- 48
circ_datadf <- as.data.frame(circ_data)
survfit(Surv(time = circ_data$RFS.months, event = circ_data$RFS.Event)~ctDNA.Dynamics, data = circ_data)Call: survfit(formula = Surv(time = circ_data$RFS.months, event = circ_data$RFS.Event) ~
ctDNA.Dynamics, data = circ_data)
n events median 0.95LCL 0.95UCL
ctDNA.Dynamics=Converted Negative 12 10 13.57 8.94 NA
ctDNA.Dynamics=Converted Positive 11 11 5.43 4.60 NA
ctDNA.Dynamics=Persistently Negative 56 34 19.06 13.84 NA
ctDNA.Dynamics=Persistently Positive 10 10 5.10 2.40 NAevent_summary <- circ_data %>%
group_by(ctDNA.Dynamics) %>%
summarise(
Total = n(),
Events = sum(RFS.Event),
Fraction = Events / n(),
Percentage = (Events / n()) * 100
)
print(event_summary)surv_object <-Surv(time = circ_data$RFS.months, event = circ_data$RFS.Event)
KM_curve <- survfit(surv_object ~ ctDNA.Dynamics, data = circ_data,conf.int=0.95,conf.type="log-log")
ggsurvplot(KM_curve, data = circ_data, pval = FALSE, conf.int = FALSE, risk.table = TRUE, break.time.by=6, palette=c("blue","green","purple","red"), title="ctDNA Dynamics | Pre-treatment - On-treatment", ylab= "Disease Free Survival", xlab="Time from Surgery (Months)", legend.title="")
summary(KM_curve, times= c(12, 24))Call: survfit(formula = surv_object ~ ctDNA.Dynamics, data = circ_data,
conf.int = 0.95, conf.type = "log-log")
ctDNA.Dynamics=Converted Negative
time n.risk n.event survival std.err lower 95% CI upper 95% CI
12 6 6 0.500 0.144 0.208 0.736
24 5 1 0.417 0.142 0.152 0.665
ctDNA.Dynamics=Converted Positive
time n.risk n.event survival std.err lower 95% CI upper 95% CI
12.0000 2.0000 9.0000 0.1818 0.1163 0.0285 0.4417
ctDNA.Dynamics=Persistently Negative
time n.risk n.event survival std.err lower 95% CI upper 95% CI
12 36 19 0.656 0.0640 0.515 0.765
24 26 9 0.489 0.0677 0.351 0.613
ctDNA.Dynamics=Persistently Positive
time n.risk n.event survival std.err lower 95% CI upper 95% CI
12.00000 1.00000 9.00000 0.10000 0.09487 0.00572 0.35813 circ_data$ctDNA.Dynamics <- factor(circ_data$ctDNA.Dynamics, levels=c("Persistently Negative","Converted Negative","Converted Positive", "Persistently Positive"))
cox_fit <- coxph(surv_object ~ ctDNA.Dynamics, data=circ_data)
ggforest(cox_fit,data = circ_data) 
summary(cox_fit)Call:
coxph(formula = surv_object ~ ctDNA.Dynamics, data = circ_data)
n= 89, number of events= 65
coef exp(coef) se(coef) z Pr(>|z|)
ctDNA.DynamicsConverted Negative 0.3528 1.4230 0.3607 0.978 0.328
ctDNA.DynamicsConverted Positive 1.7567 5.7935 0.3684 4.768 1.86e-06 ***
ctDNA.DynamicsPersistently Positive 1.9013 6.6944 0.3869 4.914 8.94e-07 ***
---
Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1
exp(coef) exp(-coef) lower .95 upper .95
ctDNA.DynamicsConverted Negative 1.423 0.7027 0.7017 2.886
ctDNA.DynamicsConverted Positive 5.794 0.1726 2.8140 11.928
ctDNA.DynamicsPersistently Positive 6.694 0.1494 3.1358 14.291
Concordance= 0.659 (se = 0.031 )
Likelihood ratio test= 30.66 on 3 df, p=1e-06
Wald test = 35.98 on 3 df, p=8e-08
Score (logrank) test = 44.86 on 3 df, p=1e-09#OS ctDNA Dynamics
rm(list=ls())
setwd("~/Downloads")
circ_data <- read.csv("ASAN_ClinicalData_GL_082023.csv")
circ_data$OS.months=circ_data$OS.months-2
circ_data <- circ_data[circ_data$OS.months>=0,]
circ_datadf <- as.data.frame(circ_data)
survfit(Surv(time = circ_data$OS.months, event = circ_data$OS.Event)~ctDNA.Dynamics, data = circ_data)Call: survfit(formula = Surv(time = circ_data$OS.months, event = circ_data$OS.Event) ~
ctDNA.Dynamics, data = circ_data)
n events median 0.95LCL 0.95UCL
ctDNA.Dynamics=Converted Negative 12 7 52.7 27.57 NA
ctDNA.Dynamics=Converted Positive 11 11 21.3 16.17 NA
ctDNA.Dynamics=Persistently Negative 56 28 48.4 29.28 NA
ctDNA.Dynamics=Persistently Positive 10 10 24.0 9.66 NAevent_summary <- circ_data %>%
group_by(ctDNA.Dynamics) %>%
summarise(
Total = n(),
Events = sum(OS.Event),
Fraction = Events / n(),
Percentage = (Events / n()) * 100
)
print(event_summary)surv_object <-Surv(time = circ_data$OS.months, event = circ_data$OS.Event)
KM_curve <- survfit(surv_object ~ ctDNA.Dynamics, data = circ_data,conf.int=0.95,conf.type="log-log")
ggsurvplot(KM_curve, data = circ_data, pval = FALSE, conf.int = FALSE, risk.table = TRUE, break.time.by=6, palette=c("purple","green","blue","red"), title="MRD Dynamics | Pre-treatment - On-treatment", ylab= "Overall Survival", xlab="Time from Surgery (Months)", legend.title="")
summary(KM_curve, times= c(12, 24))Call: survfit(formula = surv_object ~ ctDNA.Dynamics, data = circ_data,
conf.int = 0.95, conf.type = "log-log")
ctDNA.Dynamics=Converted Negative
time n.risk n.event survival std.err lower 95% CI upper 95% CI
12 11 1 0.917 0.0798 0.539 0.988
24 9 2 0.750 0.1250 0.408 0.912
ctDNA.Dynamics=Converted Positive
time n.risk n.event survival std.err lower 95% CI upper 95% CI
12 10 1 0.909 0.0867 0.508 0.987
24 5 5 0.455 0.1501 0.167 0.707
ctDNA.Dynamics=Persistently Negative
time n.risk n.event survival std.err lower 95% CI upper 95% CI
12 55 1 0.982 0.0177 0.880 0.997
24 45 10 0.804 0.0531 0.673 0.886
ctDNA.Dynamics=Persistently Positive
time n.risk n.event survival std.err lower 95% CI upper 95% CI
12 6 4 0.6 0.155 0.253 0.827
24 5 1 0.5 0.158 0.184 0.753circ_data$ctDNA.Dynamics <- factor(circ_data$ctDNA.Dynamics, levels=c("Persistently Negative","Converted Negative","Converted Positive", "Persistently Positive"))
cox_fit <- coxph(surv_object ~ ctDNA.Dynamics, data=circ_data)
ggforest(cox_fit,data = circ_data) 
summary(cox_fit)Call:
coxph(formula = surv_object ~ ctDNA.Dynamics, data = circ_data)
n= 89, number of events= 56
coef exp(coef) se(coef) z Pr(>|z|)
ctDNA.DynamicsConverted Negative 0.07171 1.07435 0.42352 0.169 0.865537
ctDNA.DynamicsConverted Positive 1.10013 3.00455 0.36133 3.045 0.002329 **
ctDNA.DynamicsPersistently Positive 1.37436 3.95256 0.37891 3.627 0.000287 ***
---
Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1
exp(coef) exp(-coef) lower .95 upper .95
ctDNA.DynamicsConverted Negative 1.074 0.9308 0.4684 2.464
ctDNA.DynamicsConverted Positive 3.005 0.3328 1.4799 6.100
ctDNA.DynamicsPersistently Positive 3.953 0.2530 1.8808 8.306
Concordance= 0.61 (se = 0.035 )
Likelihood ratio test= 16.29 on 3 df, p=0.001
Wald test = 18.53 on 3 df, p=3e-04
Score (logrank) test = 21 on 3 df, p=1e-04#OP for pts converted positive during ACT
setwd("~/Downloads")
clinstage<- read.csv("ASAN_Cholangio_OP.csv")
clinstage_df<- as.data.frame(clinstage)
clinstage_df <- clinstage_df[clinstage_df$ctDNA.Dynamics=="Converted Positive",]
##Overview plot - stratified by Stage
oplot_stratify <-swimmer_plot(df=clinstage_df,
id='PatientName',
end='fu.diff.months',
#name_fill='Arm',
col="gray",
alpha=0.75,
width=.01,
base_size = 14)
oplot_stratify <- oplot_stratify + theme(panel.border = element_blank())
oplot_stratify <- oplot_stratify + scale_y_continuous(breaks = seq(0, 108, by = 6))
oplot_stratify <- oplot_stratify + labs(x ="Patients" , y="Months from Surgery")
oplot_stratify
##plot events
oplot_ev3 <- oplot_stratify + swimmer_points(df_points=clinstage_df,
id='PatientName',
time='date.diff.months',
name_shape ='Event_type',
name_col = 'Event',
size=3.5,fill='black',
#col='darkgreen'
)
oplot_ev3Warning: The shape palette can deal with a maximum of 6 discrete values because more than 6 becomes difficult to discriminate
ℹ you have requested 9 values. Consider specifying shapes manually if you need that many have them.
Warning: Removed 55 rows containing missing values or values outside the scale range (`geom_point()`).
#Shape customization to Event_type
oplot_ev3.1 <- oplot_ev3 + ggplot2::scale_shape_manual(name="Event_type",values=c(1,16,6,18,4, 5, 23, 7, 15),breaks=c('ctDNA_neg', 'ctDNA_pos', 'Imaging', 'Surgery', 'Death', "cea_neg", "cea_pos", "ca19_neg", "ca19_pos"))
oplot_ev3.1Warning: Removed 11 rows containing missing values or values outside the scale range (`geom_point()`).
#plot treatment
oplot_ev4 <- oplot_ev3.1 + swimmer_lines(df_lines=clinstage_df,
id='PatientName',
start='Tx_start.months',
end='Tx_end.months',
name_col='Tx_type',
size=3.5,
name_alpha = 1.0)
oplot_ev4 <- oplot_ev4 + guides(linetype = guide_legend(override.aes = list(size = 5, color = "black")))
oplot_ev4 Warning: Removed 11 rows containing missing values or values outside the scale range (`geom_point()`).
Warning: Removed 133 rows containing missing values or values outside the scale range (`geom_segment()`).
#colour customization
# orange=ACT, Black=Death, Red=PD, ctDNA negative=white, ctDNA positive=black, Surgery=blue, TURBT=gray
oplot_ev4.2 <- oplot_ev4 + ggplot2::scale_color_manual(name="Event",values=c( "orange", "purple", "blue", "black", "black", "red", "blue", "blue"))
oplot_ev4.2Warning: Removed 11 rows containing missing values or values outside the scale range (`geom_point()`).
Warning: Removed 133 rows containing missing values or values outside the scale range (`geom_segment()`).
#ctDNA clearance proportions by chemotherapy regimen
rm(list=ls())
setwd("~/Downloads")
circ_data <- read.csv("ASAN_ClinicalData_GL_082023.csv")
circ_data <- circ_data[circ_data$ctDNA.MRD=="POSITIVE",]
circ_data$ctDNA.Dynamics <- factor(circ_data$ctDNA.Dynamics, levels = c("Converted Negative", "Persistently Positive"), labels = c("Clearance", "No Clearance"))
circ_data$Chemo <- factor(circ_data$Chemo, levels = c("CAP", "GemCis"))
contingency_table <- table(circ_data$Chemo, circ_data$ctDNA.Dynamics)
chi_square_test <- chisq.test(contingency_table)Warning in chisq.test(contingency_table) :
Chi-squared approximation may be incorrectprint(chi_square_test)
Pearson's Chi-squared test with Yates' continuity correction
data: contingency_table
X-squared = 0.26481, df = 1, p-value = 0.6068fisher_exact_test <- fisher.test(contingency_table)
print(fisher_exact_test)
Fisher's Exact Test for Count Data
data: contingency_table
p-value = 0.4149
alternative hypothesis: true odds ratio is not equal to 1
95 percent confidence interval:
0.3023403 20.3968347
sample estimates:
odds ratio
2.24368 print(contingency_table)
Clearance No Clearance
CAP 6 3
GemCis 6 7table_df <- as.data.frame(contingency_table)
table_df$Total <- ave(table_df$Freq, table_df$Var1, FUN = sum)
table_df$Percentage <- table_df$Freq / table_df$Total
table_df$MiddlePercentage <- table_df$Percentage / 2
ggplot(table_df, aes(x = Var1, y = Percentage, fill = Var2)) +
geom_bar(stat = "identity") +
geom_text(aes(y = MiddlePercentage, label = Freq), position = "stack", color = "black", vjust = 1.5, size = 7) +
theme_minimal() +
labs(title = "ctDNA clearance by Regimen",
x = "Regimen",
y = "Patients (%)",
fill = "ctDNA Clearance",
caption = paste("Fisher's exact test p-value: ", format.pval(fisher_exact_test$p.value))) +
scale_y_continuous(labels = scales::percent_format()) +
scale_fill_manual(values = c("Clearance" = "blue", "No Clearance" = "red")) + # define custom colors
theme(axis.text.x = element_text(angle = 0, hjust = 1.5, size = 14), # increase x-axis text size
axis.text.y = element_text(size = 14, color = "black"), # increase y-axis text size
axis.title.x = element_text(size = 14, color = "black"), # increase x-axis label size
axis.title.y = element_text(size = 14, color = "black"), # increase y-axis label size
legend.text = element_text(size = 12, color = "black")) # increase Progression label size
#Prognostic role of ctDNA C5D1 on Chemotherapy - 4 groups
rm(list=ls())
setwd("~/Downloads")
circ_data <- read.csv("ASAN_ClinicalData_GL_082023.csv")
circ_data <- subset(circ_data, !is.na(ctDNA.C5D1))
circ_data$RFS.months=circ_data$RFS.months-2
circ_data <- circ_data[circ_data$RFS.months>=0,]
circ_data$RFS.months[circ_data$RFS.months > 60] <- 60
circ_data$ctDNA.C5D1.Chemo <- NA #first we create the variable for the ctDNA & NAC combination, and we assign values
circ_data <- circ_data %>%
mutate(ctDNA.C5D1.Chemo = case_when(
Chemo == "CAP" & ctDNA.C5D1 == "NEGATIVE" ~ 1,
Chemo == "CAP" & ctDNA.C5D1 == "POSITIVE" ~ 2,
Chemo == "GemCis" & ctDNA.C5D1 == "NEGATIVE" ~ 3,
Chemo == "GemCis" & ctDNA.C5D1 == "POSITIVE" ~ 4
))
circ_datadf <- as.data.frame(circ_data)
survfit(Surv(time = circ_data$RFS.months, event = circ_data$RFS.Event)~ctDNA.C5D1.Chemo, data = circ_data)Call: survfit(formula = Surv(time = circ_data$RFS.months, event = circ_data$RFS.Event) ~
ctDNA.C5D1.Chemo, data = circ_data)
1 observation deleted due to missingness
n events median 0.95LCL 0.95UCL
ctDNA.C5D1.Chemo=1 36 24 15.58 11.57 NA
ctDNA.C5D1.Chemo=2 8 8 3.95 2.27 NA
ctDNA.C5D1.Chemo=3 35 24 16.79 11.96 38.7
ctDNA.C5D1.Chemo=4 9 9 4.83 3.59 NAevent_summary <- circ_data %>%
group_by(ctDNA.C5D1.Chemo) %>%
summarise(
Total = n(),
Events = sum(RFS.Event),
Fraction = Events / n(),
Percentage = (Events / n()) * 100
)
print(event_summary)surv_object <-Surv(time = circ_data$RFS.months, event = circ_data$RFS.Event)
KM_curve <- survfit(surv_object ~ ctDNA.C5D1.Chemo, data = circ_data,conf.int=0.95,conf.type="log-log")
ggsurvplot(KM_curve, data = circ_data, pval = FALSE, conf.int = FALSE, risk.table = TRUE, break.time.by=12, palette=c("blue","green","purple", "red"), title="DFS - ctDNA C5D1 & Chemotherapy Regimen", ylab= "Disease Free Survival", xlab="Months from surgery", legend.labs=c("CAP & ctDNA(-)", "CAP & ctDNA(+)","GemCis & ctDNA(-)", "GemCis & ctDNA(+)"), legend.title="")
summary(KM_curve, times= c(0, 12, 24))Call: survfit(formula = surv_object ~ ctDNA.C5D1.Chemo, data = circ_data,
conf.int = 0.95, conf.type = "log-log")
1 observation deleted due to missingness
ctDNA.C5D1.Chemo=1
time n.risk n.event survival std.err lower 95% CI upper 95% CI
0 36 0 1.000 0.0000 1.000 1.000
12 21 14 0.602 0.0827 0.422 0.741
24 14 7 0.401 0.0829 0.241 0.556
ctDNA.C5D1.Chemo=2
time n.risk n.event survival std.err lower 95% CI upper 95% CI
0 8 0 1 0 1 1
ctDNA.C5D1.Chemo=3
time n.risk n.event survival std.err lower 95% CI upper 95% CI
0 35 0 1.000 0.0000 1.000 1.000
12 22 13 0.629 0.0817 0.448 0.765
24 16 5 0.481 0.0851 0.309 0.634
ctDNA.C5D1.Chemo=4
time n.risk n.event survival std.err lower 95% CI upper 95% CI
0 9 0 1.000 0.000 1.00000 1.000
12 1 8 0.111 0.105 0.00613 0.388circ_data$ctDNA.C5D1.Chemo <- factor(circ_data$ctDNA.C5D1.Chemo, levels=c("1","2","3","4"))
cox_fit <- coxph(surv_object ~ ctDNA.C5D1.Chemo, data=circ_data)
ggforest(cox_fit,data = circ_data) 
summary(cox_fit)Call:
coxph(formula = surv_object ~ ctDNA.C5D1.Chemo, data = circ_data)
n= 88, number of events= 65
(1 observation deleted due to missingness)
coef exp(coef) se(coef) z Pr(>|z|)
ctDNA.C5D1.Chemo2 2.34287 10.41111 0.45548 5.144 2.69e-07 ***
ctDNA.C5D1.Chemo3 0.00918 1.00922 0.28914 0.032 0.975
ctDNA.C5D1.Chemo4 1.87019 6.48950 0.41611 4.494 6.97e-06 ***
---
Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1
exp(coef) exp(-coef) lower .95 upper .95
ctDNA.C5D1.Chemo2 10.411 0.09605 4.2637 25.422
ctDNA.C5D1.Chemo3 1.009 0.99086 0.5726 1.779
ctDNA.C5D1.Chemo4 6.490 0.15409 2.8709 14.669
Concordance= 0.639 (se = 0.035 )
Likelihood ratio test= 32.54 on 3 df, p=4e-07
Wald test = 40.73 on 3 df, p=7e-09
Score (logrank) test = 55.87 on 3 df, p=4e-12cox_fit_summary <- summary(cox_fit)#Prognostic role of ctDNA C8D1 on Chemotherapy - 4 groups
rm(list=ls())
setwd("~/Downloads")
circ_data <- read.csv("ASAN_ClinicalData_GL_082023.csv")
circ_data <- subset(circ_data, !is.na(ctDNA.C8D1))
circ_data$RFS.months=circ_data$RFS.months-2
circ_data <- circ_data[circ_data$RFS.months>=0,]
circ_data$RFS.months[circ_data$RFS.months > 60] <- 60
circ_data$ctDNA.C8D1.Chemo <- NA #first we create the variable for the ctDNA & NAC combination, and we assign values
circ_data <- circ_data %>%
mutate(ctDNA.C8D1.Chemo = case_when(
Chemo == "CAP" & ctDNA.C8D1 == "NEGATIVE" ~ 1,
Chemo == "CAP" & ctDNA.C8D1 == "POSITIVE" ~ 2,
Chemo == "GemCis" & ctDNA.C8D1 == "NEGATIVE" ~ 3,
Chemo == "GemCis" & ctDNA.C8D1 == "POSITIVE" ~ 4
))
circ_datadf <- as.data.frame(circ_data)
survfit(Surv(time = circ_data$RFS.months, event = circ_data$RFS.Event)~ctDNA.C8D1.Chemo, data = circ_data)Call: survfit(formula = Surv(time = circ_data$RFS.months, event = circ_data$RFS.Event) ~
ctDNA.C8D1.Chemo, data = circ_data)
12 observations deleted due to missingness
n events median 0.95LCL 0.95UCL
ctDNA.C8D1.Chemo=1 31 20 15.84 11.57 NA
ctDNA.C8D1.Chemo=2 6 6 7.86 5.43 NA
ctDNA.C8D1.Chemo=3 31 20 26.98 13.51 NA
ctDNA.C8D1.Chemo=4 9 9 6.44 4.83 NAevent_summary <- circ_data %>%
group_by(ctDNA.C8D1.Chemo) %>%
summarise(
Total = n(),
Events = sum(RFS.Event),
Fraction = Events / n(),
Percentage = (Events / n()) * 100
)
print(event_summary)surv_object <-Surv(time = circ_data$RFS.months, event = circ_data$RFS.Event)
KM_curve <- survfit(surv_object ~ ctDNA.C8D1.Chemo, data = circ_data,conf.int=0.95,conf.type="log-log")
ggsurvplot(KM_curve, data = circ_data, pval = FALSE, conf.int = FALSE, risk.table = TRUE, break.time.by=12, palette=c("blue","green","purple", "red"), title="DFS - ctDNA C8D1 & Chemotherapy Regimen", ylab= "Disease Free Survival", xlab="Months from surgery", legend.labs=c("CAP & ctDNA(-)", "CAP & ctDNA(+)","GemCis & ctDNA(-)", "GemCis & ctDNA(+)"), legend.title="")
summary(KM_curve, times= c(0, 12, 24))Call: survfit(formula = surv_object ~ ctDNA.C8D1.Chemo, data = circ_data,
conf.int = 0.95, conf.type = "log-log")
12 observations deleted due to missingness
ctDNA.C8D1.Chemo=1
time n.risk n.event survival std.err lower 95% CI upper 95% CI
0 31 0 1.000 0.0000 1.000 1.000
12 19 12 0.613 0.0875 0.420 0.758
24 13 6 0.419 0.0886 0.247 0.583
ctDNA.C8D1.Chemo=2
time n.risk n.event survival std.err lower 95% CI upper 95% CI
0 6 0 1.000 0.000 1.00000 1.000
12 1 5 0.167 0.152 0.00772 0.517
ctDNA.C8D1.Chemo=3
time n.risk n.event survival std.err lower 95% CI upper 95% CI
0 31 0 1.000 0.000 1.000 1.000
12 21 10 0.677 0.084 0.484 0.812
24 16 4 0.545 0.090 0.355 0.700
ctDNA.C8D1.Chemo=4
time n.risk n.event survival std.err lower 95% CI upper 95% CI
0 9 0 1.000 0.000 1.0000 1.000
12 2 7 0.222 0.139 0.0337 0.513circ_data$ctDNA.C8D1.Chemo <- factor(circ_data$ctDNA.C8D1.Chemo, levels=c("1","2","3","4"))
cox_fit <- coxph(surv_object ~ ctDNA.C8D1.Chemo, data=circ_data)
ggforest(cox_fit,data = circ_data) 
summary(cox_fit)Call:
coxph(formula = surv_object ~ ctDNA.C8D1.Chemo, data = circ_data)
n= 77, number of events= 55
(12 observations deleted due to missingness)
coef exp(coef) se(coef) z Pr(>|z|)
ctDNA.C8D1.Chemo2 1.59481 4.92738 0.48599 3.282 0.00103 **
ctDNA.C8D1.Chemo3 -0.06279 0.93914 0.31645 -0.198 0.84271
ctDNA.C8D1.Chemo4 1.64389 5.17525 0.41910 3.922 8.76e-05 ***
---
Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1
exp(coef) exp(-coef) lower .95 upper .95
ctDNA.C8D1.Chemo2 4.9274 0.2029 1.9008 12.773
ctDNA.C8D1.Chemo3 0.9391 1.0648 0.5051 1.746
ctDNA.C8D1.Chemo4 5.1753 0.1932 2.2761 11.767
Concordance= 0.638 (se = 0.037 )
Likelihood ratio test= 20.58 on 3 df, p=1e-04
Wald test = 25.53 on 3 df, p=1e-05
Score (logrank) test = 31.47 on 3 df, p=7e-07cox_fit_summary <- summary(cox_fit)#Prognostic role of ctDNA C5D1 on Primary Site - 4 groups
rm(list=ls())
setwd("~/Downloads")
circ_data <- read.csv("ASAN_ClinicalData_GL_082023.csv")
circ_data <- subset(circ_data, !is.na(ctDNA.C5D1))
circ_data$RFS.months=circ_data$RFS.months-2
circ_data <- circ_data[circ_data$RFS.months>=0,]
circ_data$RFS.months[circ_data$RFS.months > 60] <- 60
circ_data$ctDNA.C5D1.PrimSite <- NA #first we create the variable for the ctDNA & NAC combination, and we assign values
circ_data <- circ_data %>%
mutate(ctDNA.C5D1.PrimSite = case_when(
PrimSite == "dCCA" & ctDNA.C5D1 == "NEGATIVE" ~ 1,
PrimSite == "dCCA" & ctDNA.C5D1 == "POSITIVE" ~ 2,
PrimSite == "pCCA" & ctDNA.C5D1 == "NEGATIVE" ~ 3,
PrimSite == "pCCA" & ctDNA.C5D1 == "POSITIVE" ~ 4
))
circ_datadf <- as.data.frame(circ_data)
survfit(Surv(time = circ_data$RFS.months, event = circ_data$RFS.Event)~ctDNA.C5D1.PrimSite, data = circ_data)Call: survfit(formula = Surv(time = circ_data$RFS.months, event = circ_data$RFS.Event) ~
ctDNA.C5D1.PrimSite, data = circ_data)
1 observation deleted due to missingness
n events median 0.95LCL 0.95UCL
ctDNA.C5D1.PrimSite=1 37 20 16.79 11.96 NA
ctDNA.C5D1.PrimSite=2 8 8 3.88 2.27 NA
ctDNA.C5D1.PrimSite=3 34 28 15.71 11.63 27.3
ctDNA.C5D1.PrimSite=4 9 9 4.80 3.59 NAevent_summary <- circ_data %>%
group_by(ctDNA.C5D1.PrimSite) %>%
summarise(
Total = n(),
Events = sum(RFS.Event),
Fraction = Events / n(),
Percentage = (Events / n()) * 100
)
print(event_summary)surv_object <-Surv(time = circ_data$RFS.months, event = circ_data$RFS.Event)
KM_curve <- survfit(surv_object ~ ctDNA.C5D1.PrimSite, data = circ_data,conf.int=0.95,conf.type="log-log")
ggsurvplot(KM_curve, data = circ_data, pval = FALSE, conf.int = FALSE, risk.table = TRUE, break.time.by=12, palette=c("blue","green","purple", "red"), title="DFS - ctDNA C5D1 & Primary Site", ylab= "Disease Free Survival", xlab="Months from surgery", legend.labs=c("dCCA & ctDNA(-)", "dCCA & ctDNA(+)","pCCA & ctDNA(-)", "pCCA & ctDNA(+)"), legend.title="")
summary(KM_curve, times= c(0, 12, 24))Call: survfit(formula = surv_object ~ ctDNA.C5D1.PrimSite, data = circ_data,
conf.int = 0.95, conf.type = "log-log")
1 observation deleted due to missingness
ctDNA.C5D1.PrimSite=1
time n.risk n.event survival std.err lower 95% CI upper 95% CI
0 37 0 1.000 0.0000 1.000 1.000
12 22 14 0.613 0.0811 0.435 0.749
24 17 4 0.498 0.0838 0.327 0.648
ctDNA.C5D1.PrimSite=2
time n.risk n.event survival std.err lower 95% CI upper 95% CI
0 8 0 1 0 1 1
ctDNA.C5D1.PrimSite=3
time n.risk n.event survival std.err lower 95% CI upper 95% CI
0 34 0 1.000 0.0000 1.000 1.000
12 21 13 0.618 0.0833 0.434 0.757
24 13 8 0.382 0.0833 0.223 0.540
ctDNA.C5D1.PrimSite=4
time n.risk n.event survival std.err lower 95% CI upper 95% CI
0 9 0 1.000 0.000 1.00000 1.000
12 1 8 0.111 0.105 0.00613 0.388circ_data$ctDNA.C5D1.PrimSite <- factor(circ_data$ctDNA.C5D1.PrimSite, levels=c("1","2","3","4"))
cox_fit <- coxph(surv_object ~ ctDNA.C5D1.PrimSite, data=circ_data)
ggforest(cox_fit,data = circ_data) 
summary(cox_fit)Call:
coxph(formula = surv_object ~ ctDNA.C5D1.PrimSite, data = circ_data)
n= 88, number of events= 65
(1 observation deleted due to missingness)
coef exp(coef) se(coef) z Pr(>|z|)
ctDNA.C5D1.PrimSite2 2.4864 12.0174 0.4637 5.362 8.24e-08 ***
ctDNA.C5D1.PrimSite3 0.5269 1.6937 0.2948 1.787 0.0739 .
ctDNA.C5D1.PrimSite4 2.2230 9.2352 0.4307 5.162 2.45e-07 ***
---
Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1
exp(coef) exp(-coef) lower .95 upper .95
ctDNA.C5D1.PrimSite2 12.017 0.08321 4.8428 29.821
ctDNA.C5D1.PrimSite3 1.694 0.59042 0.9503 3.019
ctDNA.C5D1.PrimSite4 9.235 0.10828 3.9706 21.480
Concordance= 0.665 (se = 0.035 )
Likelihood ratio test= 35.17 on 3 df, p=1e-07
Wald test = 41.85 on 3 df, p=4e-09
Score (logrank) test = 56.32 on 3 df, p=4e-12cox_fit_summary <- summary(cox_fit)#Prognostic role of ctDNA C8D1 on Primary Site - 4 groups
rm(list=ls())
setwd("~/Downloads")
circ_data <- read.csv("ASAN_ClinicalData_GL_082023.csv")
circ_data <- subset(circ_data, !is.na(ctDNA.C8D1))
circ_data$RFS.months=circ_data$RFS.months-2
circ_data <- circ_data[circ_data$RFS.months>=0,]
circ_data$RFS.months[circ_data$RFS.months > 60] <- 60
circ_data$ctDNA.C8D1.PrimSite <- NA #first we create the variable for the ctDNA & NAC combination, and we assign values
circ_data <- circ_data %>%
mutate(ctDNA.C8D1.PrimSite = case_when(
PrimSite == "dCCA" & ctDNA.C8D1 == "NEGATIVE" ~ 1,
PrimSite == "dCCA" & ctDNA.C8D1 == "POSITIVE" ~ 2,
PrimSite == "pCCA" & ctDNA.C8D1 == "NEGATIVE" ~ 3,
PrimSite == "pCCA" & ctDNA.C8D1 == "POSITIVE" ~ 4
))
circ_datadf <- as.data.frame(circ_data)
survfit(Surv(time = circ_data$RFS.months, event = circ_data$RFS.Event)~ctDNA.C8D1.PrimSite, data = circ_data)Call: survfit(formula = Surv(time = circ_data$RFS.months, event = circ_data$RFS.Event) ~
ctDNA.C8D1.PrimSite, data = circ_data)
12 observations deleted due to missingness
n events median 0.95LCL 0.95UCL
ctDNA.C8D1.PrimSite=1 31 15 38.71 13.51 NA
ctDNA.C8D1.PrimSite=2 8 8 7.50 6.44 NA
ctDNA.C8D1.PrimSite=3 31 25 15.84 10.98 27.6
ctDNA.C8D1.PrimSite=4 7 7 5.43 4.60 NAevent_summary <- circ_data %>%
group_by(ctDNA.C8D1.PrimSite) %>%
summarise(
Total = n(),
Events = sum(RFS.Event),
Fraction = Events / n(),
Percentage = (Events / n()) * 100
)
print(event_summary)surv_object <-Surv(time = circ_data$RFS.months, event = circ_data$RFS.Event)
KM_curve <- survfit(surv_object ~ ctDNA.C8D1.PrimSite, data = circ_data,conf.int=0.95,conf.type="log-log")
ggsurvplot(KM_curve, data = circ_data, pval = FALSE, conf.int = FALSE, risk.table = TRUE, break.time.by=12, palette=c("blue","green","purple", "red"), title="DFS - ctDNA C8D1 & Primary Site", ylab= "Disease Free Survival", xlab="Months from surgery", legend.labs=c("dCCA & ctDNA(-)", "dCCA & ctDNA(+)","pCCA & ctDNA(-)", "pCCA & ctDNA(+)"), legend.title="")
summary(KM_curve, times= c(0, 12, 24))Call: survfit(formula = surv_object ~ ctDNA.C8D1.PrimSite, data = circ_data,
conf.int = 0.95, conf.type = "log-log")
12 observations deleted due to missingness
ctDNA.C8D1.PrimSite=1
time n.risk n.event survival std.err lower 95% CI upper 95% CI
0 31 0 1.000 0.000 1.000 1.000
12 21 10 0.677 0.084 0.484 0.812
24 17 3 0.579 0.089 0.387 0.730
ctDNA.C8D1.PrimSite=2
time n.risk n.event survival std.err lower 95% CI upper 95% CI
0 8 0 1.000 0.000 1.00000 1.000
12 1 7 0.125 0.117 0.00659 0.423
ctDNA.C8D1.PrimSite=3
time n.risk n.event survival std.err lower 95% CI upper 95% CI
0 31 0 1.000 0.0000 1.00 1.000
12 19 12 0.613 0.0875 0.42 0.758
24 12 7 0.387 0.0875 0.22 0.551
ctDNA.C8D1.PrimSite=4
time n.risk n.event survival std.err lower 95% CI upper 95% CI
0 7 0 1.000 0.000 1.0000 1.000
12 2 5 0.286 0.171 0.0411 0.612circ_data$ctDNA.C8D1.PrimSite <- factor(circ_data$ctDNA.C8D1.PrimSite, levels=c("1","2","3","4"))
cox_fit <- coxph(surv_object ~ ctDNA.C8D1.PrimSite, data=circ_data)
ggforest(cox_fit,data = circ_data) 
summary(cox_fit)Call:
coxph(formula = surv_object ~ ctDNA.C8D1.PrimSite, data = circ_data)
n= 77, number of events= 55
(12 observations deleted due to missingness)
coef exp(coef) se(coef) z Pr(>|z|)
ctDNA.C8D1.PrimSite2 2.1501 8.5860 0.4665 4.609 4.05e-06 ***
ctDNA.C8D1.PrimSite3 0.7532 2.1239 0.3301 2.282 0.0225 *
ctDNA.C8D1.PrimSite4 2.0206 7.5431 0.4794 4.215 2.50e-05 ***
---
Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1
exp(coef) exp(-coef) lower .95 upper .95
ctDNA.C8D1.PrimSite2 8.586 0.1165 3.441 21.424
ctDNA.C8D1.PrimSite3 2.124 0.4708 1.112 4.056
ctDNA.C8D1.PrimSite4 7.543 0.1326 2.948 19.303
Concordance= 0.671 (se = 0.036 )
Likelihood ratio test= 26.02 on 3 df, p=9e-06
Wald test = 28.78 on 3 df, p=2e-06
Score (logrank) test = 35.81 on 3 df, p=8e-08cox_fit_summary <- summary(cox_fit)---
title: "ASAN Cholangio_GL_Final analysis 092024"
output: html_notebook
---
library(swimplot)
library(grid)
library(gtable)
library(readr) 
library(mosaic)
library(dplyr) 
library(survival) 
library(survminer) 
library(ggplot2)
library(scales)
library(coxphf)
library(ggthemes)
library(tidyverse)
library(gtsummary)
library(flextable)
library(parameters)
library(car)
library(ComplexHeatmap)
library(rms)

#ctDNA positivity by stage and window
```{r}
#ctDNA at MRD
rm(list=ls())
setwd("~/Downloads")
circ_data <- read.csv("ASAN_ClinicalData_GL_082023.csv")
circ_data <- circ_data[circ_data$ctDNA.MRD!="",]
circ_data$ctDNA.MRD <- factor(circ_data$ctDNA.MRD, levels=c("NEGATIVE","POSITIVE"))
circ_data <- subset(circ_data, ctDNA.MRD %in% c("NEGATIVE", "POSITIVE"))
circ_data$Stage <- factor(circ_data$Stage, levels=c("II","III","IV"))
positive_counts_by_stage <- aggregate(circ_data$ctDNA.MRD == "POSITIVE", by=list(circ_data$Stage), FUN=sum)
total_counts_by_stage <- aggregate(circ_data$ctDNA.MRD, by=list(circ_data$Stage), FUN=length)
combined_data <- data.frame(
  Stage = total_counts_by_stage$Group.1,
  Total_Count = total_counts_by_stage$x,
  Positive_Count = positive_counts_by_stage$x,
  Rate = (positive_counts_by_stage$x / total_counts_by_stage$x) * 100  # Convert to percentage
)
combined_data$Rate <- sprintf("%.2f%%", combined_data$Rate)
overall_total_count <- nrow(circ_data)
overall_positive_count <- nrow(circ_data[circ_data$ctDNA.MRD == "POSITIVE",])
overall_positivity_rate <- (overall_positive_count / overall_total_count) * 100  # Convert to percentage
overall_row <- data.frame(
  Stage = "Overall",
  Total_Count = overall_total_count,
  Positive_Count = overall_positive_count,
  Rate = sprintf("%.2f%%", overall_positivity_rate)
)
combined_data <- rbind(combined_data, overall_row)
print(combined_data)

#ctDNA C5D1
rm(list=ls())
setwd("~/Downloads")
circ_data <- read.csv("ASAN_ClinicalData_GL_082023.csv")
circ_data <- circ_data[circ_data$ctDNA.C5D1!="",]
circ_data$ctDNA.C5D1 <- factor(circ_data$ctDNA.C5D1, levels=c("NEGATIVE","POSITIVE"))
circ_data <- subset(circ_data, ctDNA.C5D1 %in% c("NEGATIVE", "POSITIVE"))
circ_data$Stage <- factor(circ_data$Stage, levels=c("II","III","IV"))
positive_counts_by_stage <- aggregate(circ_data$ctDNA.C5D1 == "POSITIVE", by=list(circ_data$Stage), FUN=sum)
total_counts_by_stage <- aggregate(circ_data$ctDNA.C5D1, by=list(circ_data$Stage), FUN=length)
combined_data <- data.frame(
  Stage = total_counts_by_stage$Group.1,
  Total_Count = total_counts_by_stage$x,
  Positive_Count = positive_counts_by_stage$x,
  Rate = (positive_counts_by_stage$x / total_counts_by_stage$x) * 100  # Convert to percentage
)
combined_data$Rate <- sprintf("%.2f%%", combined_data$Rate)
overall_total_count <- nrow(circ_data)
overall_positive_count <- nrow(circ_data[circ_data$ctDNA.C5D1 == "POSITIVE",])
overall_positivity_rate <- (overall_positive_count / overall_total_count) * 100  # Convert to percentage
overall_row <- data.frame(
  Stage = "Overall",
  Total_Count = overall_total_count,
  Positive_Count = overall_positive_count,
  Rate = sprintf("%.2f%%", overall_positivity_rate)
)
combined_data <- rbind(combined_data, overall_row)
print(combined_data)

#ctDNA C8D1
rm(list=ls())
setwd("~/Downloads")
circ_data <- read.csv("ASAN_ClinicalData_GL_082023.csv")
circ_data <- circ_data[circ_data$ctDNA.C8D1!="",]
circ_data$ctDNA.C8D1 <- factor(circ_data$ctDNA.C8D1, levels=c("NEGATIVE","POSITIVE"))
circ_data <- subset(circ_data, ctDNA.C8D1 %in% c("NEGATIVE", "POSITIVE"))
circ_data$Stage <- factor(circ_data$Stage, levels=c("II","III","IV"))
positive_counts_by_stage <- aggregate(circ_data$ctDNA.C8D1 == "POSITIVE", by=list(circ_data$Stage), FUN=sum)
total_counts_by_stage <- aggregate(circ_data$ctDNA.C8D1, by=list(circ_data$Stage), FUN=length)
combined_data <- data.frame(
  Stage = total_counts_by_stage$Group.1,
  Total_Count = total_counts_by_stage$x,
  Positive_Count = positive_counts_by_stage$x,
  Rate = (positive_counts_by_stage$x / total_counts_by_stage$x) * 100  # Convert to percentage
)
combined_data$Rate <- sprintf("%.2f%%", combined_data$Rate)
overall_total_count <- nrow(circ_data)
overall_positive_count <- nrow(circ_data[circ_data$ctDNA.C8D1 == "POSITIVE",])
overall_positivity_rate <- (overall_positive_count / overall_total_count) * 100  # Convert to percentage
overall_row <- data.frame(
  Stage = "Overall",
  Total_Count = overall_total_count,
  Positive_Count = overall_positive_count,
  Rate = sprintf("%.2f%%", overall_positivity_rate)
)
combined_data <- rbind(combined_data, overall_row)
print(combined_data)

#ctDNA EOT
rm(list=ls())
setwd("~/Downloads")
circ_data <- read.csv("ASAN_ClinicalData_GL_082023.csv")
circ_data <- circ_data[circ_data$ctDNA.EOT!="",]
circ_data$ctDNA.EOT <- factor(circ_data$ctDNA.EOT, levels=c("NEGATIVE","POSITIVE"))
circ_data <- subset(circ_data, ctDNA.EOT %in% c("NEGATIVE", "POSITIVE"))
circ_data$Stage <- factor(circ_data$Stage, levels=c("II","III","IV"))
positive_counts_by_stage <- aggregate(circ_data$ctDNA.EOT == "POSITIVE", by=list(circ_data$Stage), FUN=sum)
total_counts_by_stage <- aggregate(circ_data$ctDNA.EOT, by=list(circ_data$Stage), FUN=length)
combined_data <- data.frame(
  Stage = total_counts_by_stage$Group.1,
  Total_Count = total_counts_by_stage$x,
  Positive_Count = positive_counts_by_stage$x,
  Rate = (positive_counts_by_stage$x / total_counts_by_stage$x) * 100  # Convert to percentage
)
combined_data$Rate <- sprintf("%.2f%%", combined_data$Rate)
overall_total_count <- nrow(circ_data)
overall_positive_count <- nrow(circ_data[circ_data$ctDNA.EOT == "POSITIVE",])
overall_positivity_rate <- (overall_positive_count / overall_total_count) * 100  # Convert to percentage
overall_row <- data.frame(
  Stage = "Overall",
  Total_Count = overall_total_count,
  Positive_Count = overall_positive_count,
  Rate = sprintf("%.2f%%", overall_positivity_rate)
)
combined_data <- rbind(combined_data, overall_row)
print(combined_data)

#ctDNA anytime
rm(list=ls())
setwd("~/Downloads")
circ_data <- read.csv("ASAN_ClinicalData_GL_082023.csv")
circ_data <- circ_data[circ_data$ctDNA.anytime!="",]
circ_data$ctDNA.anytime <- factor(circ_data$ctDNA.anytime, levels=c("NEGATIVE","POSITIVE"))
circ_data <- subset(circ_data, ctDNA.anytime %in% c("NEGATIVE", "POSITIVE"))
circ_data$Stage <- factor(circ_data$Stage, levels=c("II","III","IV"))
positive_counts_by_stage <- aggregate(circ_data$ctDNA.anytime == "POSITIVE", by=list(circ_data$Stage), FUN=sum)
total_counts_by_stage <- aggregate(circ_data$ctDNA.anytime, by=list(circ_data$Stage), FUN=length)
combined_data <- data.frame(
  Stage = total_counts_by_stage$Group.1,
  Total_Count = total_counts_by_stage$x,
  Positive_Count = positive_counts_by_stage$x,
  Rate = (positive_counts_by_stage$x / total_counts_by_stage$x) * 100  # Convert to percentage
)
combined_data$Rate <- sprintf("%.2f%%", combined_data$Rate)
overall_total_count <- nrow(circ_data)
overall_positive_count <- nrow(circ_data[circ_data$ctDNA.anytime == "POSITIVE",])
overall_positivity_rate <- (overall_positive_count / overall_total_count) * 100  # Convert to percentage
overall_row <- data.frame(
  Stage = "Overall",
  Total_Count = overall_total_count,
  Positive_Count = overall_positive_count,
  Rate = sprintf("%.2f%%", overall_positivity_rate)
)
combined_data <- rbind(combined_data, overall_row)
print(combined_data)
```


#Heatmap for clinicopathologic factors
```{r}
rm(list=ls())
setwd("~/Downloads") 
circ_data <- read.csv("ASAN_ClinicalData_GL_082023.csv")
circ_datadf <- as.data.frame(circ_data)
circ_data <- circ_data %>% arrange(Stage)
circ_datadf <- as.data.frame(circ_data)

ha <- HeatmapAnnotation(
  Stage = circ_data$Stage,
  Sex = circ_data$Sex,
  PrimSite = circ_data$PrimSite,
  pT = circ_data$pT,
  Pathology = circ_data$Pathology,
  Chemo = circ_data$Chemo,
  ctDNA.MRD = circ_data$ctDNA.MRD,
  ctDNA.C5D1 = circ_data$ctDNA.C5D1,
  ctDNA.C8D1 = circ_data$ctDNA.C8D1,
  ctDNA.EOT = circ_data$ctDNA.EOT,
  ctDNA.anytime = circ_data$ctDNA.anytime,
  RecStatus = circ_data$RecStatus,
  VitalStatus = circ_data$VitalStatus,
  
    col = list(Stage = c("II" = "seagreen2", "III" = "orange", "IV" = "purple"),
    Sex = c("Female" = "goldenrod" , "Male" = "blue4"),
    PrimSite = c("pCCA" = "darkgreen", "dCCA" ="#008BCE"),
    pT = c("T1" = "lightblue", "T2" ="orange", "T3" = "brown" ),
    Pathology = c("G1" = "yellow3", "G2" ="darkgreen", "G3" = "brown2"),
    Chemo = c("CAP" = "lightblue", "GemCis" = "orange2"),
    ctDNA.MRD = c("POSITIVE" = "red3", "NEGATIVE" ="blue"),
    ctDNA.C5D1 = c("POSITIVE" = "red3", "NEGATIVE" ="blue"),
    ctDNA.C8D1 = c("POSITIVE" = "red3", "NEGATIVE" ="blue"),
    ctDNA.EOT = c("POSITIVE" = "red3", "NEGATIVE" ="blue"),
    ctDNA.anytime = c("POSITIVE" = "red3", "NEGATIVE" ="blue"),
    RFS.Event = c("TRUE" = "red3", "FALSE" ="blue"),
    OS.Event = c("TRUE" = "black", "FALSE" ="grey")
)
)
ht <- Heatmap(matrix(nrow = 0, ncol = length(circ_data$Stage)),show_row_names = FALSE,cluster_rows = F,cluster_columns = FALSE, top_annotation = ha)
pdf("heatmap.pdf",width = 7, height = 7)
draw(ht, annotation_legend_side = "bottom")
dev.off()
```


#Prognostic role of ctDNA at the MRD time point
```{r}
rm(list=ls())
setwd("~/Downloads") 
circ_data <- read.csv("ASAN_ClinicalData_GL_082023.csv")
circ_data$RFS.months=circ_data$RFS.months-2
circ_data <- circ_data[circ_data$RFS.months>=0,]
circ_data$RFS.months[circ_data$RFS.months > 60] <- 60
circ_datadf <- as.data.frame(circ_data)

survfit(Surv(time = circ_data$RFS.months, event = circ_data$RFS.Event)~ctDNA.MRD, data = circ_data)
event_summary <- circ_data %>%
  group_by(ctDNA.MRD) %>%
  summarise(
    Total = n(),
    Events = sum(RFS.Event),
    Fraction = Events / n(),
    Percentage = (Events / n()) * 100
  )
print(event_summary)
surv_object <-Surv(time = circ_data$RFS.months, event = circ_data$RFS.Event)
KM_curve <- survfit(surv_object ~ ctDNA.MRD, data = circ_data,conf.int=0.95,conf.type="log-log") 
ggsurvplot(KM_curve, data = circ_data, pval = FALSE, conf.int = FALSE, risk.table = TRUE, break.time.by=12, palette=c("blue","red"), title="DFS - ctDNA MRD timepoint", ylab= "Disease Free Survival", xlab="Time (Months)", legend.labs=c("ctDNA Negative", "ctDNA Positive"), legend.title="")
summary(KM_curve, times= c(12, 24))
circ_data$ctDNA.MRD <- factor(circ_data$ctDNA.MRD, levels=c("NEGATIVE","POSITIVE"))
cox_fit <- coxph(surv_object ~ ctDNA.MRD, data=circ_data) 
ggforest(cox_fit,data = circ_data) 
summary(cox_fit)
cox_fit_summary <- summary(cox_fit)

# Extract values for HR, 95% CI, and p-value
HR <- cox_fit_summary$coefficients[2]
lower_CI <- cox_fit_summary$conf.int[3]
upper_CI <- cox_fit_summary$conf.int[4]
p_value <- cox_fit_summary$coefficients[5]
label_text <- paste0("HR = ", round(HR, 2), " (", round(lower_CI, 2), "-", round(upper_CI, 2), "); p = ", round(p_value, 3))
print(label_text)
```


#OS by ctDNA at the MRD time point
```{r}
rm(list=ls())
setwd("~/Downloads") 
circ_data <- read.csv("ASAN_ClinicalData_GL_082023.csv")
circ_data$OS.months=circ_data$OS.months-2
circ_data <- circ_data[circ_data$OS.months>=0,]
circ_datadf <- as.data.frame(circ_data)

survfit(Surv(time = circ_data$OS.months, event = circ_data$OS.Event)~ctDNA.MRD, data = circ_data)
event_summary <- circ_data %>%
  group_by(ctDNA.MRD) %>%
  summarise(
    Total = n(),
    Events = sum(OS.Event),
    Fraction = Events / n(),
    Percentage = (Events / n()) * 100
  )
print(event_summary)
surv_object <-Surv(time = circ_data$OS.months, event = circ_data$OS.Event)
KM_curve <- survfit(surv_object ~ ctDNA.MRD, data = circ_data,conf.int=0.95,conf.type="log-log") 
ggsurvplot(KM_curve, data = circ_data, pval = FALSE, conf.int = FALSE, risk.table = TRUE, break.time.by=12, palette=c("blue","red"), title="OS - ctDNA MRD timepoint", ylab= "Overall Survival", xlab="Time (Months)", legend.labs=c("ctDNA Negative", "ctDNA Positive"), legend.title="")
summary(KM_curve, times= c(12, 24))
circ_data$ctDNA.MRD <- factor(circ_data$ctDNA.MRD, levels=c("NEGATIVE","POSITIVE"))
cox_fit <- coxph(surv_object ~ ctDNA.MRD, data=circ_data) 
ggforest(cox_fit,data = circ_data) 
summary(cox_fit)
cox_fit_summary <- summary(cox_fit)

# Extract values for HR, 95% CI, and p-value
HR <- cox_fit_summary$coefficients[2]
lower_CI <- cox_fit_summary$conf.int[3]
upper_CI <- cox_fit_summary$conf.int[4]
p_value <- cox_fit_summary$coefficients[5]
label_text <- paste0("HR = ", round(HR, 2), " (", round(lower_CI, 2), "-", round(upper_CI, 2), "); p = ", round(p_value, 3))
print(label_text)
```


#Association of ctDNA MRD MTM levels with clinicopathological factors
```{r}
rm(list=ls())
setwd("~/Downloads") 
circ_data <- read.csv("ASAN_ClinicalData_GL_082023.csv")

tally(~pN, data=circ_data, margins = TRUE)
circ_data$pN <- factor(circ_data$pN, levels = c("N1","N2"), labels = c("pN1 (n=69)","pN2 (n=20)"))
boxplot(ctDNA.MRD.MTM~pN, data=circ_data, main="ctDNA MRD MTM - pN", xlab="pN", ylab="MTM/mL", col="white",border="black", ylim=c(0, 10))
m1<-wilcox.test(ctDNA.MRD.MTM ~ pN, data=circ_data, na.rm=TRUE, exact=FALSE, conf.int=TRUE)
print(m1)

tally(~ResMarg, data=circ_data, margins = TRUE)
circ_data$ResMarg <- factor(circ_data$ResMarg, levels = c("R0","R1"), labels = c("R0 (n=60)","R1 (n=29)"))
boxplot(ctDNA.MRD.MTM~ResMarg, data=circ_data, main="ctDNA MRD MTM - Resection Margin", 
        xlab="ResMarg", ylab="MTM/mL", col="white", border="black", ylim=c(0, 10))
m2 <- wilcox.test(ctDNA.MRD.MTM ~ ResMarg, data=circ_data, na.rm=TRUE, exact=FALSE, conf.int=TRUE)
print(m2)

tally(~PrimSite, data=circ_data, margins = TRUE)
circ_data$PrimSite <- factor(circ_data$PrimSite, levels = c("pCCA","dCCA"), labels = c("pCCA (n=43)","dCCA (n=46)"))
boxplot(ctDNA.MRD.MTM~PrimSite, data=circ_data, main="ctDNA MRD MTM - Primary Site", 
        xlab="Primary Site", ylab="MTM/mL", col="white", border="black", ylim=c(0, 10))
m3 <- wilcox.test(ctDNA.MRD.MTM ~ PrimSite, data=circ_data, na.rm=TRUE, exact=FALSE, conf.int=TRUE)
print(m3)

tally(~Stage, data=circ_data, margins = TRUE)
circ_data$ctDNA.MRD.MTM <- as.numeric(as.character(circ_data$ctDNA.MRD.MTM))
circ_data$Stage <- factor(circ_data$Stage, levels = c("II","III","IV"), labels = c("II (n=37)", "III (n=40)","IV (n=12)"))
boxplot(ctDNA.MRD.MTM~Stage, data=circ_data, main="ctDNA MRD MTM - Stage", 
        xlab="Stage", ylab="MTM/mL", col="white", border="black", ylim=c(0, 10))
kruskal_result <- kruskal.test(ctDNA.MRD.MTM ~ Stage, data=circ_data)
print(kruskal_result)
pairwise_wilcox <- pairwise.wilcox.test(circ_data$ctDNA.MRD.MTM, circ_data$Stage, 
                                        p.adjust.method = "BH", na.rm = TRUE)
print(pairwise_wilcox)

tally(~CA19.MRD, data=circ_data, margins = TRUE)
circ_data$CA19.MRD <- factor(circ_data$CA19.MRD, levels = c("Normal","Elevated"), labels = c("Normal (n=74)","Elevated (n=15)"))
boxplot(ctDNA.MRD.MTM~CA19.MRD, data=circ_data, main="ctDNA MRD MTM - CA 19-9", 
        xlab="CA 19-9", ylab="MTM/mL", col="white", border="black", ylim=c(0, 10))
m4 <- wilcox.test(ctDNA.MRD.MTM ~ CA19.MRD, data=circ_data, na.rm=TRUE, exact=FALSE, conf.int=TRUE)
print(m4)
```


#Median MTM/mL levels for ctDNA positive pts at MRD
```{r}
rm(list=ls())
setwd("~/Downloads") 
circ_data <- read.csv("ASAN_ClinicalData_GL_082023.csv")
circ_data <- circ_data[circ_data$ctDNA.MRD=="POSITIVE",]

median_ctDNA <- median(circ_data$ctDNA.MRD.MTM, na.rm = TRUE)
range_ctDNA <- range(circ_data$ctDNA.MRD.MTM, na.rm = TRUE)
cat("Median MTM/mL post-surgery:", median_ctDNA, "\n")
cat("Range MTM/mL post-surgery:", range_ctDNA[1], "-", range_ctDNA[2], "\n")
```


#Association of ctDNA MRD status with clinicopathological factors
```{r}
rm(list=ls())
setwd("~/Downloads") 
circ_data <- read.csv("ASAN_ClinicalData_GL_082023.csv")

circ_data$ctDNA.MRD <- factor(circ_data$ctDNA.MRD, levels = c("NEGATIVE", "POSITIVE"), labels = c("ctDNA(-)", "ctDNA(+)"))
circ_data$pN <- factor(circ_data$pN, levels = c("N1", "N2"), labels = c("pN1", "pN2"))
contingency_table <- table(circ_data$pN, circ_data$ctDNA.MRD)
chi_square_test <- chisq.test(contingency_table)
print(chi_square_test)
fisher_exact_test <- fisher.test(contingency_table)
print(fisher_exact_test)
print(contingency_table)
table_df <- as.data.frame(contingency_table)
table_df$Total <- ave(table_df$Freq, table_df$Var1, FUN = sum)
table_df$Percentage <- table_df$Freq / table_df$Total
table_df$MiddlePercentage <- table_df$Percentage / 2
ggplot(table_df, aes(x = Var1, y = Percentage, fill = Var2)) +
  geom_bar(stat = "identity") +
  geom_text(aes(y = MiddlePercentage, label = Freq), position = "stack", color = "black", vjust = 1.5, size = 7) +
  theme_minimal() +
  labs(title = "ctDNA status - pN", 
       x = "pN", 
       y = "Patients (%)", 
       fill = "ctDNA MRD",
       caption = paste("Fisher's exact test p-value: ", format.pval(fisher_exact_test$p.value))) +
  scale_y_continuous(labels = scales::percent_format()) +
  scale_fill_manual(values = c("ctDNA(-)" = "blue", "ctDNA(+)" = "red")) + # define custom colors
  theme(axis.text.x = element_text(angle = 0, hjust = 1.5, size = 14), # increase x-axis text size
        axis.text.y = element_text(size = 14, color = "black"), # increase y-axis text size
        axis.title.x = element_text(size = 14, color = "black"), # increase x-axis label size
        axis.title.y = element_text(size = 14, color = "black"), # increase y-axis label size
        legend.text = element_text(size = 12, color = "black"))  # increase Progression label size

rm(list=ls())
setwd("~/Downloads") 
circ_data <- read.csv("ASAN_ClinicalData_GL_082023.csv")

circ_data$ctDNA.MRD <- factor(circ_data$ctDNA.MRD, levels = c("NEGATIVE", "POSITIVE"), labels = c("ctDNA(-)", "ctDNA(+)"))
circ_data$ResMarg <- factor(circ_data$ResMarg, levels = c("R0", "R1"), labels = c("R0", "R1"))
contingency_table <- table(circ_data$ResMarg, circ_data$ctDNA.MRD)
chi_square_test <- chisq.test(contingency_table)
print(chi_square_test)
fisher_exact_test <- fisher.test(contingency_table)
print(fisher_exact_test)
print(contingency_table)
table_df <- as.data.frame(contingency_table)
table_df$Total <- ave(table_df$Freq, table_df$Var1, FUN = sum)
table_df$Percentage <- table_df$Freq / table_df$Total
table_df$MiddlePercentage <- table_df$Percentage / 2
ggplot(table_df, aes(x = Var1, y = Percentage, fill = Var2)) +
  geom_bar(stat = "identity") +
  geom_text(aes(y = MiddlePercentage, label = Freq), position = "stack", color = "black", vjust = 1.5, size = 7) +
  theme_minimal() +
  labs(title = "ctDNA status - Margins", 
       x = "Margins", 
       y = "Patients (%)", 
       fill = "ctDNA MRD",
       caption = paste("Fisher's exact test p-value: ", format.pval(fisher_exact_test$p.value))) +
  scale_y_continuous(labels = scales::percent_format()) +
  scale_fill_manual(values = c("ctDNA(-)" = "blue", "ctDNA(+)" = "red")) + # define custom colors
  theme(axis.text.x = element_text(angle = 0, hjust = 1.5, size = 14), # increase x-axis text size
        axis.text.y = element_text(size = 14, color = "black"), # increase y-axis text size
        axis.title.x = element_text(size = 14, color = "black"), # increase x-axis label size
        axis.title.y = element_text(size = 14, color = "black"), # increase y-axis label size
        legend.text = element_text(size = 12, color = "black"))  # increase Progression label size

rm(list=ls())
setwd("~/Downloads") 
circ_data <- read.csv("ASAN_ClinicalData_GL_082023.csv")

circ_data$ctDNA.MRD <- factor(circ_data$ctDNA.MRD, levels = c("NEGATIVE", "POSITIVE"), labels = c("ctDNA(-)", "ctDNA(+)"))
circ_data$PrimSite <- factor(circ_data$PrimSite, levels = c("pCCA", "dCCA"), labels = c("pCCA", "dCCA"))
contingency_table <- table(circ_data$PrimSite, circ_data$ctDNA.MRD)
chi_square_test <- chisq.test(contingency_table)
print(chi_square_test)
fisher_exact_test <- fisher.test(contingency_table)
print(fisher_exact_test)
print(contingency_table)
table_df <- as.data.frame(contingency_table)
table_df$Total <- ave(table_df$Freq, table_df$Var1, FUN = sum)
table_df$Percentage <- table_df$Freq / table_df$Total
table_df$MiddlePercentage <- table_df$Percentage / 2
ggplot(table_df, aes(x = Var1, y = Percentage, fill = Var2)) +
  geom_bar(stat = "identity") +
  geom_text(aes(y = MiddlePercentage, label = Freq), position = "stack", color = "black", vjust = 1.5, size = 7) +
  theme_minimal() +
  labs(title = "ctDNA status - Primary Site", 
       x = "Primary Site", 
       y = "Patients (%)", 
       fill = "ctDNA MRD",
       caption = paste("Fisher's exact test p-value: ", format.pval(fisher_exact_test$p.value))) +
  scale_y_continuous(labels = scales::percent_format()) +
  scale_fill_manual(values = c("ctDNA(-)" = "blue", "ctDNA(+)" = "red")) + # define custom colors
  theme(axis.text.x = element_text(angle = 0, hjust = 1.5, size = 14), # increase x-axis text size
        axis.text.y = element_text(size = 14, color = "black"), # increase y-axis text size
        axis.title.x = element_text(size = 14, color = "black"), # increase x-axis label size
        axis.title.y = element_text(size = 14, color = "black"), # increase y-axis label size
        legend.text = element_text(size = 12, color = "black"))  # increase Progression label size

rm(list=ls())
setwd("~/Downloads") 
circ_data <- read.csv("ASAN_ClinicalData_GL_082023.csv")

circ_data$ctDNA.MRD <- factor(circ_data$ctDNA.MRD, levels = c("NEGATIVE", "POSITIVE"), labels = c("ctDNA(-)", "ctDNA(+)"))
circ_data$Stage <- factor(circ_data$Stage, levels = c("II", "III", "IV"), labels = c("II", "III", "IV"))
contingency_table <- table(circ_data$Stage, circ_data$ctDNA.MRD)
chi_square_test <- chisq.test(contingency_table)
print(chi_square_test)
fisher_exact_test <- fisher.test(contingency_table)
print(fisher_exact_test)
print(contingency_table)
table_df <- as.data.frame(contingency_table)
table_df$Total <- ave(table_df$Freq, table_df$Var1, FUN = sum)
table_df$Percentage <- table_df$Freq / table_df$Total
table_df$MiddlePercentage <- table_df$Percentage / 2
ggplot(table_df, aes(x = Var1, y = Percentage, fill = Var2)) +
  geom_bar(stat = "identity") +
  geom_text(aes(y = MiddlePercentage, label = Freq), position = "stack", color = "black", vjust = 1.5, size = 7) +
  theme_minimal() +
  labs(title = "ctDNA status - Stage", 
       x = "Stage", 
       y = "Patients (%)", 
       fill = "ctDNA MRD",
       caption = paste("Fisher's exact test p-value: ", format.pval(fisher_exact_test$p.value))) +
  scale_y_continuous(labels = scales::percent_format()) +
  scale_fill_manual(values = c("ctDNA(-)" = "blue", "ctDNA(+)" = "red")) + # define custom colors
  theme(axis.text.x = element_text(angle = 0, hjust = 1.5, size = 14), # increase x-axis text size
        axis.text.y = element_text(size = 14, color = "black"), # increase y-axis text size
        axis.title.x = element_text(size = 14, color = "black"), # increase x-axis label size
        axis.title.y = element_text(size = 14, color = "black"), # increase y-axis label size
        legend.text = element_text(size = 12, color = "black"))  # increase Progression label size

pairwise_fisher <- function(data, factor1, factor2) {
  levels <- unique(data[[factor1]])
  results <- data.frame(Stage1 = character(), Stage2 = character(), p.value = numeric(), stringsAsFactors = FALSE)
  
  for (i in 1:(length(levels) - 1)) {
    for (j in (i + 1):length(levels)) {
      subset_data <- data %>% filter(data[[factor1]] %in% c(levels[i], levels[j]))
      contingency_table_pairwise <- table(subset_data[[factor1]], subset_data[[factor2]])
      fisher_result <- fisher.test(contingency_table_pairwise)
      results <- rbind(results, data.frame(Stage1 = levels[i], Stage2 = levels[j], p.value = fisher_result$p.value))
    }
  }
  return(results)
}

# Perform pairwise comparisons
pairwise_results <- pairwise_fisher(circ_data, "Stage", "ctDNA.MRD")
print(pairwise_results)

rm(list=ls())
setwd("~/Downloads") 
circ_data <- read.csv("ASAN_ClinicalData_GL_082023.csv")

circ_data$ctDNA.MRD <- factor(circ_data$ctDNA.MRD, levels = c("NEGATIVE", "POSITIVE"), labels = c("ctDNA(-)", "ctDNA(+)"))
circ_data$CA19.MRD <- factor(circ_data$CA19.MRD, levels = c("Normal", "Elevated"), labels = c("Normal", "Elevated"))
contingency_table <- table(circ_data$CA19.MRD, circ_data$ctDNA.MRD)
chi_square_test <- chisq.test(contingency_table)
print(chi_square_test)
fisher_exact_test <- fisher.test(contingency_table)
print(fisher_exact_test)
print(contingency_table)
table_df <- as.data.frame(contingency_table)
table_df$Total <- ave(table_df$Freq, table_df$Var1, FUN = sum)
table_df$Percentage <- table_df$Freq / table_df$Total
table_df$MiddlePercentage <- table_df$Percentage / 2
ggplot(table_df, aes(x = Var1, y = Percentage, fill = Var2)) +
  geom_bar(stat = "identity") +
  geom_text(aes(y = MiddlePercentage, label = Freq), position = "stack", color = "black", vjust = 1.5, size = 7) +
  theme_minimal() +
  labs(title = "ctDNA status - CA 19-9", 
       x = "CA 19-9", 
       y = "Patients (%)", 
       fill = "ctDNA MRD",
       caption = paste("Fisher's exact test p-value: ", format.pval(fisher_exact_test$p.value))) +
  scale_y_continuous(labels = scales::percent_format()) +
  scale_fill_manual(values = c("ctDNA(-)" = "blue", "ctDNA(+)" = "red")) + # define custom colors
  theme(axis.text.x = element_text(angle = 0, hjust = 1.5, size = 14), # increase x-axis text size
        axis.text.y = element_text(size = 14, color = "black"), # increase y-axis text size
        axis.title.x = element_text(size = 14, color = "black"), # increase x-axis label size
        axis.title.y = element_text(size = 14, color = "black"), # increase y-axis label size
        legend.text = element_text(size = 12, color = "black"))  # increase Progression label size
```

#Prognostic role of ctDNA at C5D1
```{r}
rm(list=ls())
setwd("~/Downloads") 
circ_data <- read.csv("ASAN_ClinicalData_GL_082023.csv")
circ_data <- circ_data[circ_data$ctDNA.C5D1!="",]
circ_data$RFS.months=circ_data$RFS.months-2
circ_data <- circ_data[circ_data$RFS.months>=0,]
circ_data$RFS.months[circ_data$RFS.months > 60] <- 60
circ_datadf <- as.data.frame(circ_data)

survfit(Surv(time = circ_data$RFS.months, event = circ_data$RFS.Event)~ctDNA.C5D1, data = circ_data)
event_summary <- circ_data %>%
  group_by(ctDNA.C5D1) %>%
  summarise(
    Total = n(),
    Events = sum(RFS.Event),
    Fraction = Events / n(),
    Percentage = (Events / n()) * 100
  )
print(event_summary)
surv_object <-Surv(time = circ_data$RFS.months, event = circ_data$RFS.Event)
KM_curve <- survfit(surv_object ~ ctDNA.C5D1, data = circ_data,conf.int=0.95,conf.type="log-log") 
ggsurvplot(KM_curve, data = circ_data, pval = FALSE, conf.int = FALSE, risk.table = TRUE, break.time.by=12, palette=c("blue","red"), title="DFS - ctDNA C5D1", ylab= "Disease Free Survival", xlab="Time (Months)", legend.labs=c("ctDNA Negative", "ctDNA Positive"), legend.title="")
summary(KM_curve, times= c(12, 24))
circ_data$ctDNA.C5D1 <- factor(circ_data$ctDNA.C5D1, levels=c("NEGATIVE","POSITIVE"))
cox_fit <- coxph(surv_object ~ ctDNA.C5D1, data=circ_data) 
ggforest(cox_fit,data = circ_data) 
summary(cox_fit)
cox_fit_summary <- summary(cox_fit)

# Extract values for HR, 95% CI, and p-value
HR <- cox_fit_summary$coefficients[2]
lower_CI <- cox_fit_summary$conf.int[3]
upper_CI <- cox_fit_summary$conf.int[4]
p_value <- cox_fit_summary$coefficients[5]
label_text <- paste0("HR = ", round(HR, 2), " (", round(lower_CI, 2), "-", round(upper_CI, 2), "); p = ", round(p_value, 3))
print(label_text)
```


#Multivariate regression model for DFS with ctDNA and CA 19-9
```{r}
rm(list=ls())
setwd("~/Downloads") 
circ_data <- read.csv("ASAN_ClinicalData_GL_082023.csv")
circ_data <- subset(circ_data, !is.na(ctDNA.C5D1))
circ_data$RFS.months=circ_data$RFS.months-2
circ_data <- circ_data[circ_data$RFS.months>=0,]
circ_data$RFS.months[circ_data$RFS.months > 60] <- 60
circ_datadf <- as.data.frame(circ_data)
circ_datadf$Sex <- factor(circ_datadf$Sex, levels = c("Female", "Male"), labels = c("Female", "Male"))
circ_datadf$PrimSite <- factor(circ_datadf$PrimSite, levels = c("pCCA", "dCCA"), labels = c("pCCA", "dCCA"))
circ_datadf$Chemo <- factor(circ_datadf$Chemo, levels = c("CAP", "GemCis"), labels = c("CAP", "GemCis"))
circ_datadf$ResMarg <- factor(circ_datadf$ResMarg, levels = c("R0", "R1"))
circ_datadf$Stage <- factor(circ_datadf$Stage, levels = c("II", "III", "IV"), labels = c("II", "III", "IV"))
circ_datadf$TP53 <- factor(circ_datadf$TP53, levels = c("WT", "Mut"))
circ_datadf$CA19.C5D1 <- factor(circ_datadf$CA19.C5D1, levels = c("Normal", "Elevated"))
circ_datadf$ctDNA.C5D1 <- factor(circ_datadf$ctDNA.C5D1, levels = c("NEGATIVE", "POSITIVE"), labels = c("Negative", "Positive"))
surv_object<-Surv(time = circ_datadf$RFS.months, event = circ_datadf$RFS.Event) 
cox_fit <- coxph(surv_object ~ Sex + Age + PrimSite + Stage + Chemo + ResMarg + CA19.C5D1 + ctDNA.C5D1, data=circ_datadf) 
ggforest(cox_fit, data = circ_datadf, main = "Multivariate Regression Model for DFS - Landmark analysis", refLabel = "Reference Group")
test.ph <- cox.zph(cox_fit)
```

#Univariate regression model for factors used at the C5D1 MVA
```{r}
rm(list=ls())
setwd("~/Downloads") 
circ_data <- read.csv("ASAN_ClinicalData_GL_082023.csv")
circ_data <- subset(circ_data, !is.na(ctDNA.C5D1))
circ_data$RFS.months=circ_data$RFS.months-2
circ_data <- circ_data[circ_data$RFS.months>=0,]
circ_data$RFS.months[circ_data$RFS.months > 60] <- 60
surv_object <-Surv(time = circ_data$RFS.months, event = circ_data$RFS.Event)
circ_data$Sex <- factor(circ_data$Sex, levels = c("Female", "Male"), labels = c("Female", "Male")) #univariate for gender
cox_fit <- coxph(surv_object ~ Sex, data=circ_data)
summary(cox_fit)
cox_fit_summary <- summary(cox_fit)
HR <- cox_fit_summary$coefficients[2]
lower_CI <- cox_fit_summary$conf.int[3]
upper_CI <- cox_fit_summary$conf.int[4]
p_value <- cox_fit_summary$coefficients[5]
label_text <- paste0("HR = ", round(HR, 2), " (", round(lower_CI, 2), "-", round(upper_CI, 2), "); p = ", round(p_value, 3))
print(label_text)

rm(list=ls())
setwd("~/Downloads") 
circ_data <- read.csv("ASAN_ClinicalData_GL_082023.csv")
circ_data <- subset(circ_data, !is.na(ctDNA.C5D1))
circ_data$RFS.months=circ_data$RFS.months-2
circ_data <- circ_data[circ_data$RFS.months>=0,]
circ_data$RFS.months[circ_data$RFS.months > 60] <- 60
surv_object <-Surv(time = circ_data$RFS.months, event = circ_data$RFS.Event)
cox_fit <- coxph(surv_object ~ Age, data=circ_data) #univariate for age
summary(cox_fit)
cox_fit_summary <- summary(cox_fit)
HR <- cox_fit_summary$coefficients[2]
lower_CI <- cox_fit_summary$conf.int[3]
upper_CI <- cox_fit_summary$conf.int[4]
p_value <- cox_fit_summary$coefficients[5]
label_text <- paste0("HR = ", round(HR, 2), " (", round(lower_CI, 2), "-", round(upper_CI, 2), "); p = ", round(p_value, 3))
print(label_text)

rm(list=ls())
setwd("~/Downloads") 
circ_data <- read.csv("ASAN_ClinicalData_GL_082023.csv")
circ_data <- subset(circ_data, !is.na(ctDNA.C5D1))
circ_data$RFS.months=circ_data$RFS.months-2
circ_data <- circ_data[circ_data$RFS.months>=0,]
circ_data$RFS.months[circ_data$RFS.months > 60] <- 60
surv_object <-Surv(time = circ_data$RFS.months, event = circ_data$RFS.Event)
circ_data$PrimSite <- factor(circ_data$PrimSite, levels = c("pCCA", "dCCA"), labels = c("pCCA", "dCCA")) #univariate for Primary Site
cox_fit <- coxph(surv_object ~ PrimSite, data=circ_data)
summary(cox_fit)
cox_fit_summary <- summary(cox_fit)
HR <- cox_fit_summary$coefficients[2]
lower_CI <- cox_fit_summary$conf.int[3]
upper_CI <- cox_fit_summary$conf.int[4]
p_value <- cox_fit_summary$coefficients[5]
label_text <- paste0("HR = ", round(HR, 2), " (", round(lower_CI, 2), "-", round(upper_CI, 2), "); p = ", round(p_value, 3))
print(label_text)

rm(list=ls())
setwd("~/Downloads") 
circ_data <- read.csv("ASAN_ClinicalData_GL_082023.csv")
circ_data <- subset(circ_data, !is.na(ctDNA.C5D1))
circ_data$RFS.months=circ_data$RFS.months-2
circ_data <- circ_data[circ_data$RFS.months>=0,]
circ_data$RFS.months[circ_data$RFS.months > 60] <- 60
surv_object <-Surv(time = circ_data$RFS.months, event = circ_data$RFS.Event)
KM_curve <- survfit(surv_object ~ Stage, data = circ_data,conf.int=0.95,conf.type="log-log") 
ggsurvplot(KM_curve, data = circ_data, pval = TRUE, conf.int = FALSE, risk.table = TRUE, break.time.by=3, palette=c("blue","green","red"), title="DFS - ctDNA C5D1 - Stage", ylab= "Disease Free Survival", xlab="Time (Months)", legend.labs=c("II", "III", "IV"), legend.title="")
circ_data$Stage <- factor(circ_data$Stage, levels = c("II", "III", "IV"), labels = c("II", "III", "IV")) #univariate for Stage
cox_fit <- coxph(surv_object ~ Stage, data=circ_data) 
ggforest(cox_fit,data = circ_data) 
summary(cox_fit)

rm(list=ls())
setwd("~/Downloads") 
circ_data <- read.csv("ASAN_ClinicalData_GL_082023.csv")
circ_data <- subset(circ_data, !is.na(ctDNA.C5D1))
circ_data$RFS.months=circ_data$RFS.months-2
circ_data <- circ_data[circ_data$RFS.months>=0,]
circ_data$RFS.months[circ_data$RFS.months > 60] <- 60
surv_object <-Surv(time = circ_data$RFS.months, event = circ_data$RFS.Event)
circ_data$Chemo <- factor(circ_data$Chemo, levels = c("CAP", "GemCis"), labels = c("CAP", "GemCis")) #univariate for Chemotherapy
cox_fit <- coxph(surv_object ~ Chemo, data=circ_data)
summary(cox_fit)
cox_fit_summary <- summary(cox_fit)
HR <- cox_fit_summary$coefficients[2]
lower_CI <- cox_fit_summary$conf.int[3]
upper_CI <- cox_fit_summary$conf.int[4]
p_value <- cox_fit_summary$coefficients[5]
label_text <- paste0("HR = ", round(HR, 2), " (", round(lower_CI, 2), "-", round(upper_CI, 2), "); p = ", round(p_value, 3))
print(label_text)

rm(list=ls())
setwd("~/Downloads") 
circ_data <- read.csv("ASAN_ClinicalData_GL_082023.csv")
circ_data <- subset(circ_data, !is.na(ctDNA.C5D1))
circ_data$RFS.months=circ_data$RFS.months-2
circ_data <- circ_data[circ_data$RFS.months>=0,]
circ_data$RFS.months[circ_data$RFS.months > 60] <- 60
surv_object <-Surv(time = circ_data$RFS.months, event = circ_data$RFS.Event)
circ_data$ResMarg <- factor(circ_data$ResMarg, levels = c("R0", "R1")) #univariate for Resection margin
cox_fit <- coxph(surv_object ~ ResMarg, data=circ_data)
summary(cox_fit)
cox_fit_summary <- summary(cox_fit)
HR <- cox_fit_summary$coefficients[2]
lower_CI <- cox_fit_summary$conf.int[3]
upper_CI <- cox_fit_summary$conf.int[4]
p_value <- cox_fit_summary$coefficients[5]
label_text <- paste0("HR = ", round(HR, 2), " (", round(lower_CI, 2), "-", round(upper_CI, 2), "); p = ", round(p_value, 3))
print(label_text)

rm(list=ls())
setwd("~/Downloads") 
circ_data <- read.csv("ASAN_ClinicalData_GL_082023.csv")
circ_data <- subset(circ_data, !is.na(ctDNA.C5D1))
circ_data$RFS.months=circ_data$RFS.months-2
circ_data <- circ_data[circ_data$RFS.months>=0,]
circ_data$RFS.months[circ_data$RFS.months > 60] <- 60
surv_object <-Surv(time = circ_data$RFS.months, event = circ_data$RFS.Event)
circ_data$CA19.C5D1 <- factor(circ_data$CA19.C5D1, levels = c("Normal", "Elevated")) #univariate for CA 19-9 C5D1
cox_fit <- coxph(surv_object ~ CA19.C5D1, data=circ_data)
summary(cox_fit)
cox_fit_summary <- summary(cox_fit)
HR <- cox_fit_summary$coefficients[2]
lower_CI <- cox_fit_summary$conf.int[3]
upper_CI <- cox_fit_summary$conf.int[4]
p_value <- cox_fit_summary$coefficients[5]
label_text <- paste0("HR = ", round(HR, 2), " (", round(lower_CI, 2), "-", round(upper_CI, 2), "); p = ", round(p_value, 3))
print(label_text)

rm(list=ls())
setwd("~/Downloads") 
circ_data <- read.csv("ASAN_ClinicalData_GL_082023.csv")
circ_data <- subset(circ_data, !is.na(ctDNA.C5D1))
circ_data$RFS.months=circ_data$RFS.months-2
circ_data <- circ_data[circ_data$RFS.months>=0,]
circ_data$RFS.months[circ_data$RFS.months > 60] <- 60
surv_object <-Surv(time = circ_data$RFS.months, event = circ_data$RFS.Event)
circ_data$ctDNA.C5D1 <- factor(circ_data$ctDNA.C5D1, levels = c("NEGATIVE", "POSITIVE"), labels = c("Negative", "Positive")) #univariate for ctDNA C5D1
cox_fit <- coxph(surv_object ~ ctDNA.C5D1, data=circ_data)
summary(cox_fit)
cox_fit_summary <- summary(cox_fit)
HR <- cox_fit_summary$coefficients[2]
lower_CI <- cox_fit_summary$conf.int[3]
upper_CI <- cox_fit_summary$conf.int[4]
p_value <- cox_fit_summary$coefficients[5]
label_text <- paste0("HR = ", round(HR, 2), " (", round(lower_CI, 2), "-", round(upper_CI, 2), "); p = ", round(p_value, 3))
print(label_text)
```

#OS by ctDNA at C5D1
```{r}
rm(list=ls())
setwd("~/Downloads") 
circ_data <- read.csv("ASAN_ClinicalData_GL_082023.csv")
circ_data <- circ_data[circ_data$ctDNA.C5D1!="",]
circ_data$OS.months=circ_data$OS.months-2
circ_data <- circ_data[circ_data$OS.months>=0,]
circ_datadf <- as.data.frame(circ_data)

survfit(Surv(time = circ_data$OS.months, event = circ_data$OS.Event)~ctDNA.C5D1, data = circ_data)
event_summary <- circ_data %>%
  group_by(ctDNA.C5D1) %>%
  summarise(
    Total = n(),
    Events = sum(OS.Event),
    Fraction = Events / n(),
    Percentage = (Events / n()) * 100
  )
print(event_summary)
surv_object <-Surv(time = circ_data$OS.months, event = circ_data$OS.Event)
KM_curve <- survfit(surv_object ~ ctDNA.C5D1, data = circ_data,conf.int=0.95,conf.type="log-log") 
ggsurvplot(KM_curve, data = circ_data, pval = FALSE, conf.int = FALSE, risk.table = TRUE, break.time.by=12, palette=c("blue","red"), title="OS - ctDNA C5D1", ylab= "Overall Survival", xlab="Time (Months)", legend.labs=c("ctDNA Negative", "ctDNA Positive"), legend.title="")
summary(KM_curve, times= c(12, 24))
circ_data$ctDNA.C5D1 <- factor(circ_data$ctDNA.C5D1, levels=c("NEGATIVE","POSITIVE"))
cox_fit <- coxph(surv_object ~ ctDNA.C5D1, data=circ_data) 
ggforest(cox_fit,data = circ_data) 
summary(cox_fit)
cox_fit_summary <- summary(cox_fit)

# Extract values for HR, 95% CI, and p-value
HR <- cox_fit_summary$coefficients[2]
lower_CI <- cox_fit_summary$conf.int[3]
upper_CI <- cox_fit_summary$conf.int[4]
p_value <- cox_fit_summary$coefficients[5]
label_text <- paste0("HR = ", round(HR, 2), " (", round(lower_CI, 2), "-", round(upper_CI, 2), "); p = ", round(p_value, 3))
print(label_text)
```


#Prognostic role of ctDNA at C8D1
```{r}
rm(list=ls())
setwd("~/Downloads") 
circ_data <- read.csv("ASAN_ClinicalData_GL_082023.csv")
circ_data <- circ_data[circ_data$ctDNA.C8D1!="",]
circ_data$RFS.months=circ_data$RFS.months-2
circ_data <- circ_data[circ_data$RFS.months>=0,]
circ_data$RFS.months[circ_data$RFS.months > 60] <- 60
circ_datadf <- as.data.frame(circ_data)

survfit(Surv(time = circ_data$RFS.months, event = circ_data$RFS.Event)~ctDNA.C8D1, data = circ_data)
event_summary <- circ_data %>%
  group_by(ctDNA.C8D1) %>%
  summarise(
    Total = n(),
    Events = sum(RFS.Event),
    Fraction = Events / n(),
    Percentage = (Events / n()) * 100
  )
print(event_summary)
surv_object <-Surv(time = circ_data$RFS.months, event = circ_data$RFS.Event)
KM_curve <- survfit(surv_object ~ ctDNA.C8D1, data = circ_data,conf.int=0.95,conf.type="log-log") 
ggsurvplot(KM_curve, data = circ_data, pval = FALSE, conf.int = FALSE, risk.table = TRUE, break.time.by=12, palette=c("blue","red"), title="DFS - ctDNA C8D1", ylab= "Disease Free Survival", xlab="Time (Months)", legend.labs=c("ctDNA Negative", "ctDNA Positive"), legend.title="")
summary(KM_curve, times= c(12, 24))
circ_data$ctDNA.C8D1 <- factor(circ_data$ctDNA.C8D1, levels=c("NEGATIVE","POSITIVE"))
cox_fit <- coxph(surv_object ~ ctDNA.C8D1, data=circ_data) 
ggforest(cox_fit,data = circ_data) 
summary(cox_fit)
cox_fit_summary <- summary(cox_fit)

# Extract values for HR, 95% CI, and p-value
HR <- cox_fit_summary$coefficients[2]
lower_CI <- cox_fit_summary$conf.int[3]
upper_CI <- cox_fit_summary$conf.int[4]
p_value <- cox_fit_summary$coefficients[5]
label_text <- paste0("HR = ", round(HR, 2), " (", round(lower_CI, 2), "-", round(upper_CI, 2), "); p = ", round(p_value, 3))
print(label_text)
```


#OS by ctDNA at C8D1
```{r}
rm(list=ls())
setwd("~/Downloads") 
circ_data <- read.csv("ASAN_ClinicalData_GL_082023.csv")
circ_data <- circ_data[circ_data$ctDNA.C8D1!="",]
circ_data$OS.months=circ_data$OS.months-2
circ_data <- circ_data[circ_data$OS.months>=0,]
circ_datadf <- as.data.frame(circ_data)

survfit(Surv(time = circ_data$OS.months, event = circ_data$OS.Event)~ctDNA.C8D1, data = circ_data)
event_summary <- circ_data %>%
  group_by(ctDNA.C8D1) %>%
  summarise(
    Total = n(),
    Events = sum(OS.Event),
    Fraction = Events / n(),
    Percentage = (Events / n()) * 100
  )
print(event_summary)
surv_object <-Surv(time = circ_data$OS.months, event = circ_data$OS.Event)
KM_curve <- survfit(surv_object ~ ctDNA.C8D1, data = circ_data,conf.int=0.95,conf.type="log-log") 
ggsurvplot(KM_curve, data = circ_data, pval = FALSE, conf.int = FALSE, risk.table = TRUE, break.time.by=12, palette=c("blue","red"), title="OS - ctDNA C8D1", ylab= "Overall Survival", xlab="Time (Months)", legend.labs=c("ctDNA Negative", "ctDNA Positive"), legend.title="")
summary(KM_curve, times= c(12, 24))
circ_data$ctDNA.C8D1 <- factor(circ_data$ctDNA.C8D1, levels=c("NEGATIVE","POSITIVE"))
cox_fit <- coxph(surv_object ~ ctDNA.C8D1, data=circ_data) 
ggforest(cox_fit,data = circ_data) 
summary(cox_fit)
cox_fit_summary <- summary(cox_fit)

# Extract values for HR, 95% CI, and p-value
HR <- cox_fit_summary$coefficients[2]
lower_CI <- cox_fit_summary$conf.int[3]
upper_CI <- cox_fit_summary$conf.int[4]
p_value <- cox_fit_summary$coefficients[5]
label_text <- paste0("HR = ", round(HR, 2), " (", round(lower_CI, 2), "-", round(upper_CI, 2), "); p = ", round(p_value, 3))
print(label_text)
```


#Prognostic role of ctDNA anytime post-surgery
```{r}
rm(list=ls())
setwd("~/Downloads") 
circ_data <- read.csv("ASAN_ClinicalData_GL_082023.csv")
circ_data <- circ_data[circ_data$ctDNA.anytime!="",]
circ_data$RFS.months=circ_data$RFS.months-2
circ_data <- circ_data[circ_data$RFS.months>=0,]
circ_data$RFS.months[circ_data$RFS.months > 60] <- 60
circ_datadf <- as.data.frame(circ_data)

survfit(Surv(time = circ_data$RFS.months, event = circ_data$RFS.Event)~ctDNA.anytime, data = circ_data)
event_summary <- circ_data %>%
  group_by(ctDNA.anytime) %>%
  summarise(
    Total = n(),
    Events = sum(RFS.Event),
    Fraction = Events / n(),
    Percentage = (Events / n()) * 100
  )
print(event_summary)
surv_object <-Surv(time = circ_data$RFS.months, event = circ_data$RFS.Event)
KM_curve <- survfit(surv_object ~ ctDNA.anytime, data = circ_data,conf.int=0.95,conf.type="log-log") 
ggsurvplot(KM_curve, data = circ_data, pval = FALSE, conf.int = FALSE, risk.table = TRUE, break.time.by=12, palette=c("blue","red"), title="DFS - ctDNA anytime post-surgery", ylab= "Disease Free Survival", xlab="Time (Months)", legend.labs=c("ctDNA Negative", "ctDNA Positive"), legend.title="")
summary(KM_curve, times= c(12, 24))
circ_data$ctDNA.anytime <- factor(circ_data$ctDNA.anytime, levels=c("NEGATIVE","POSITIVE"))
cox_fit <- coxph(surv_object ~ ctDNA.anytime, data=circ_data) 
ggforest(cox_fit,data = circ_data) 
summary(cox_fit)
cox_fit_summary <- summary(cox_fit)

# Extract values for HR, 95% CI, and p-value
HR <- cox_fit_summary$coefficients[2]
lower_CI <- cox_fit_summary$conf.int[3]
upper_CI <- cox_fit_summary$conf.int[4]
p_value <- cox_fit_summary$coefficients[5]
label_text <- paste0("HR = ", round(HR, 2), " (", round(lower_CI, 2), "-", round(upper_CI, 2), "); p = ", round(p_value, 3))
print(label_text)
```


#OS by ctDNA anytime post-surgery
```{r}
rm(list=ls())
setwd("~/Downloads") 
circ_data <- read.csv("ASAN_ClinicalData_GL_082023.csv")
circ_data <- circ_data[circ_data$ctDNA.anytime!="",]
circ_data$OS.months=circ_data$OS.months-2
circ_data <- circ_data[circ_data$OS.months>=0,]
circ_datadf <- as.data.frame(circ_data)

survfit(Surv(time = circ_data$OS.months, event = circ_data$OS.Event)~ctDNA.anytime, data = circ_data)
event_summary <- circ_data %>%
  group_by(ctDNA.anytime) %>%
  summarise(
    Total = n(),
    Events = sum(OS.Event),
    Fraction = Events / n(),
    Percentage = (Events / n()) * 100
  )
print(event_summary)
surv_object <-Surv(time = circ_data$OS.months, event = circ_data$OS.Event)
KM_curve <- survfit(surv_object ~ ctDNA.anytime, data = circ_data,conf.int=0.95,conf.type="log-log") 
ggsurvplot(KM_curve, data = circ_data, pval = FALSE, conf.int = FALSE, risk.table = TRUE, break.time.by=12, palette=c("blue","red"), title="OS - ctDNA anytime post-surgery", ylab= "Overall Survival", xlab="Time (Months)", legend.labs=c("ctDNA Negative", "ctDNA Positive"), legend.title="")
summary(KM_curve, times= c(12, 24))
circ_data$ctDNA.anytime <- factor(circ_data$ctDNA.anytime, levels=c("NEGATIVE","POSITIVE"))
cox_fit <- coxph(surv_object ~ ctDNA.anytime, data=circ_data) 
ggforest(cox_fit,data = circ_data) 
summary(cox_fit)
cox_fit_summary <- summary(cox_fit)

# Extract values for HR, 95% CI, and p-value
HR <- cox_fit_summary$coefficients[2]
lower_CI <- cox_fit_summary$conf.int[3]
upper_CI <- cox_fit_summary$conf.int[4]
p_value <- cox_fit_summary$coefficients[5]
label_text <- paste0("HR = ", round(HR, 2), " (", round(lower_CI, 2), "-", round(upper_CI, 2), "); p = ", round(p_value, 3))
print(label_text)
```


#Prognostic role of ctDNA Dynamics
```{r}
rm(list=ls())
setwd("~/Downloads") 
circ_data <- read.csv("ASAN_ClinicalData_GL_082023.csv")
circ_data$RFS.months=circ_data$RFS.months-2
circ_data <- circ_data[circ_data$RFS.months>=0,]
circ_data$RFS.months[circ_data$RFS.months > 48] <- 48
circ_datadf <- as.data.frame(circ_data)

survfit(Surv(time = circ_data$RFS.months, event = circ_data$RFS.Event)~ctDNA.Dynamics, data = circ_data)
event_summary <- circ_data %>%
  group_by(ctDNA.Dynamics) %>%
  summarise(
    Total = n(),
    Events = sum(RFS.Event),
    Fraction = Events / n(),
    Percentage = (Events / n()) * 100
  )
print(event_summary)
surv_object <-Surv(time = circ_data$RFS.months, event = circ_data$RFS.Event) 
KM_curve <- survfit(surv_object ~ ctDNA.Dynamics, data = circ_data,conf.int=0.95,conf.type="log-log") 
ggsurvplot(KM_curve, data = circ_data, pval = FALSE, conf.int = FALSE, risk.table = TRUE, break.time.by=6, palette=c("blue","green","purple","red"), title="ctDNA Dynamics | Pre-treatment - On-treatment", ylab= "Disease Free Survival", xlab="Time from Surgery (Months)", legend.title="") 
summary(KM_curve, times= c(12, 24))
circ_data$ctDNA.Dynamics <- factor(circ_data$ctDNA.Dynamics, levels=c("Persistently Negative","Converted Negative","Converted Positive", "Persistently Positive"))
cox_fit <- coxph(surv_object ~ ctDNA.Dynamics, data=circ_data)  
ggforest(cox_fit,data = circ_data) 
summary(cox_fit)
```


#OS ctDNA Dynamics
```{r}
rm(list=ls())
setwd("~/Downloads") 
circ_data <- read.csv("ASAN_ClinicalData_GL_082023.csv")
circ_data$OS.months=circ_data$OS.months-2
circ_data <- circ_data[circ_data$OS.months>=0,]
circ_datadf <- as.data.frame(circ_data)

survfit(Surv(time = circ_data$OS.months, event = circ_data$OS.Event)~ctDNA.Dynamics, data = circ_data)
event_summary <- circ_data %>%
  group_by(ctDNA.Dynamics) %>%
  summarise(
    Total = n(),
    Events = sum(OS.Event),
    Fraction = Events / n(),
    Percentage = (Events / n()) * 100
  )
print(event_summary)
surv_object <-Surv(time = circ_data$OS.months, event = circ_data$OS.Event) 
KM_curve <- survfit(surv_object ~ ctDNA.Dynamics, data = circ_data,conf.int=0.95,conf.type="log-log") 
ggsurvplot(KM_curve, data = circ_data, pval = FALSE, conf.int = FALSE, risk.table = TRUE, break.time.by=6, palette=c("purple","green","blue","red"), title="MRD Dynamics | Pre-treatment - On-treatment", ylab= "Overall Survival", xlab="Time from Surgery (Months)", legend.title="") 
summary(KM_curve, times= c(12, 24))
circ_data$ctDNA.Dynamics <- factor(circ_data$ctDNA.Dynamics, levels=c("Persistently Negative","Converted Negative","Converted Positive", "Persistently Positive"))
cox_fit <- coxph(surv_object ~ ctDNA.Dynamics, data=circ_data)  
ggforest(cox_fit,data = circ_data) 
summary(cox_fit)
```


#OP for pts converted positive during ACT
```{r}
setwd("~/Downloads") 
clinstage<- read.csv("ASAN_Cholangio_OP.csv")
clinstage_df<- as.data.frame(clinstage)
clinstage_df <- clinstage_df[clinstage_df$ctDNA.Dynamics=="Converted Positive",]

##Overview plot - stratified by Stage
oplot_stratify <-swimmer_plot(df=clinstage_df,
                              id='PatientName',
                              end='fu.diff.months',
                              #name_fill='Arm',
                              col="gray",
                              alpha=0.75,
                              width=.01,
                              base_size = 14)
oplot_stratify <- oplot_stratify + theme(panel.border = element_blank())
oplot_stratify <- oplot_stratify + scale_y_continuous(breaks = seq(0, 108, by = 6))
oplot_stratify <- oplot_stratify + labs(x ="Patients" , y="Months from Surgery")
oplot_stratify

##plot events
oplot_ev3 <- oplot_stratify + swimmer_points(df_points=clinstage_df,
                                             id='PatientName',
                                             time='date.diff.months',
                                             name_shape ='Event_type',
                                             name_col = 'Event',
                                             size=3.5,fill='black',
                                             #col='darkgreen'
)
oplot_ev3

#Shape customization to Event_type

oplot_ev3.1 <- oplot_ev3 + ggplot2::scale_shape_manual(name="Event_type",values=c(1,16,6,18,4, 5, 23, 7, 15),breaks=c('ctDNA_neg', 'ctDNA_pos', 'Imaging', 'Surgery', 'Death', "cea_neg", "cea_pos", "ca19_neg", "ca19_pos"))

oplot_ev3.1

#plot treatment

oplot_ev4 <- oplot_ev3.1 + swimmer_lines(df_lines=clinstage_df,
                                         id='PatientName',
                                         start='Tx_start.months',
                                         end='Tx_end.months',
                                         name_col='Tx_type',
                                         size=3.5,
                                         name_alpha = 1.0)
oplot_ev4 <- oplot_ev4 + guides(linetype = guide_legend(override.aes = list(size = 5, color = "black")))
oplot_ev4  


#colour customization
# orange=ACT, Black=Death, Red=PD, ctDNA negative=white, ctDNA positive=black, Surgery=blue, TURBT=gray 
oplot_ev4.2 <- oplot_ev4 + ggplot2::scale_color_manual(name="Event",values=c( "orange", "purple", "blue", "black", "black", "red", "blue", "blue"))
oplot_ev4.2
```


#ctDNA clearance proportions by chemotherapy regimen
```{r}
rm(list=ls())
setwd("~/Downloads") 
circ_data <- read.csv("ASAN_ClinicalData_GL_082023.csv")
circ_data <- circ_data[circ_data$ctDNA.MRD=="POSITIVE",]

circ_data$ctDNA.Dynamics <- factor(circ_data$ctDNA.Dynamics, levels = c("Converted Negative", "Persistently Positive"), labels = c("Clearance", "No Clearance"))
circ_data$Chemo <- factor(circ_data$Chemo, levels = c("CAP", "GemCis"))
contingency_table <- table(circ_data$Chemo, circ_data$ctDNA.Dynamics)
chi_square_test <- chisq.test(contingency_table)
print(chi_square_test)
fisher_exact_test <- fisher.test(contingency_table)
print(fisher_exact_test)
print(contingency_table)
table_df <- as.data.frame(contingency_table)
table_df$Total <- ave(table_df$Freq, table_df$Var1, FUN = sum)
table_df$Percentage <- table_df$Freq / table_df$Total
table_df$MiddlePercentage <- table_df$Percentage / 2
ggplot(table_df, aes(x = Var1, y = Percentage, fill = Var2)) +
  geom_bar(stat = "identity") +
  geom_text(aes(y = MiddlePercentage, label = Freq), position = "stack", color = "black", vjust = 1.5, size = 7) +
  theme_minimal() +
  labs(title = "ctDNA clearance by Regimen", 
       x = "Regimen", 
       y = "Patients (%)", 
       fill = "ctDNA Clearance",
       caption = paste("Fisher's exact test p-value: ", format.pval(fisher_exact_test$p.value))) +
  scale_y_continuous(labels = scales::percent_format()) +
  scale_fill_manual(values = c("Clearance" = "blue", "No Clearance" = "red")) + # define custom colors
  theme(axis.text.x = element_text(angle = 0, hjust = 1.5, size = 14), # increase x-axis text size
        axis.text.y = element_text(size = 14, color = "black"), # increase y-axis text size
        axis.title.x = element_text(size = 14, color = "black"), # increase x-axis label size
        axis.title.y = element_text(size = 14, color = "black"), # increase y-axis label size
        legend.text = element_text(size = 12, color = "black"))  # increase Progression label size
```


#Prognostic role of ctDNA C5D1 on Chemotherapy - 4 groups
```{r}
rm(list=ls())
setwd("~/Downloads") 
circ_data <- read.csv("ASAN_ClinicalData_GL_082023.csv")
circ_data <- subset(circ_data, !is.na(ctDNA.C5D1))
circ_data$RFS.months=circ_data$RFS.months-2
circ_data <- circ_data[circ_data$RFS.months>=0,]
circ_data$RFS.months[circ_data$RFS.months > 60] <- 60


circ_data$ctDNA.C5D1.Chemo <- NA #first we create the variable for the ctDNA & NAC combination, and we assign values
circ_data <- circ_data %>%
  mutate(ctDNA.C5D1.Chemo = case_when(
    Chemo == "CAP" & ctDNA.C5D1 == "NEGATIVE" ~ 1,
    Chemo == "CAP" & ctDNA.C5D1 == "POSITIVE" ~ 2,
    Chemo == "GemCis" & ctDNA.C5D1 == "NEGATIVE" ~ 3,
    Chemo == "GemCis" & ctDNA.C5D1 == "POSITIVE" ~ 4
  ))
circ_datadf <- as.data.frame(circ_data)

survfit(Surv(time = circ_data$RFS.months, event = circ_data$RFS.Event)~ctDNA.C5D1.Chemo, data = circ_data)
event_summary <- circ_data %>%
  group_by(ctDNA.C5D1.Chemo) %>%
  summarise(
    Total = n(),
    Events = sum(RFS.Event),
    Fraction = Events / n(),
    Percentage = (Events / n()) * 100
  )
print(event_summary)
surv_object <-Surv(time = circ_data$RFS.months, event = circ_data$RFS.Event)
KM_curve <- survfit(surv_object ~ ctDNA.C5D1.Chemo, data = circ_data,conf.int=0.95,conf.type="log-log") 
ggsurvplot(KM_curve, data = circ_data, pval = FALSE, conf.int = FALSE, risk.table = TRUE, break.time.by=12, palette=c("blue","green","purple", "red"), title="DFS - ctDNA C5D1 & Chemotherapy Regimen", ylab= "Disease Free Survival", xlab="Months from surgery", legend.labs=c("CAP & ctDNA(-)", "CAP & ctDNA(+)","GemCis & ctDNA(-)", "GemCis & ctDNA(+)"), legend.title="")
summary(KM_curve, times= c(0, 12, 24))
circ_data$ctDNA.C5D1.Chemo <- factor(circ_data$ctDNA.C5D1.Chemo, levels=c("1","2","3","4"))
cox_fit <- coxph(surv_object ~ ctDNA.C5D1.Chemo, data=circ_data) 
ggforest(cox_fit,data = circ_data) 
summary(cox_fit)
cox_fit_summary <- summary(cox_fit)
```


#Prognostic role of ctDNA C8D1 on Chemotherapy - 4 groups
```{r}
rm(list=ls())
setwd("~/Downloads") 
circ_data <- read.csv("ASAN_ClinicalData_GL_082023.csv")
circ_data <- subset(circ_data, !is.na(ctDNA.C8D1))
circ_data$RFS.months=circ_data$RFS.months-2
circ_data <- circ_data[circ_data$RFS.months>=0,]
circ_data$RFS.months[circ_data$RFS.months > 60] <- 60


circ_data$ctDNA.C8D1.Chemo <- NA #first we create the variable for the ctDNA & NAC combination, and we assign values
circ_data <- circ_data %>%
  mutate(ctDNA.C8D1.Chemo = case_when(
    Chemo == "CAP" & ctDNA.C8D1 == "NEGATIVE" ~ 1,
    Chemo == "CAP" & ctDNA.C8D1 == "POSITIVE" ~ 2,
    Chemo == "GemCis" & ctDNA.C8D1 == "NEGATIVE" ~ 3,
    Chemo == "GemCis" & ctDNA.C8D1 == "POSITIVE" ~ 4
  ))
circ_datadf <- as.data.frame(circ_data)

survfit(Surv(time = circ_data$RFS.months, event = circ_data$RFS.Event)~ctDNA.C8D1.Chemo, data = circ_data)
event_summary <- circ_data %>%
  group_by(ctDNA.C8D1.Chemo) %>%
  summarise(
    Total = n(),
    Events = sum(RFS.Event),
    Fraction = Events / n(),
    Percentage = (Events / n()) * 100
  )
print(event_summary)
surv_object <-Surv(time = circ_data$RFS.months, event = circ_data$RFS.Event)
KM_curve <- survfit(surv_object ~ ctDNA.C8D1.Chemo, data = circ_data,conf.int=0.95,conf.type="log-log") 
ggsurvplot(KM_curve, data = circ_data, pval = FALSE, conf.int = FALSE, risk.table = TRUE, break.time.by=12, palette=c("blue","green","purple", "red"), title="DFS - ctDNA C8D1 & Chemotherapy Regimen", ylab= "Disease Free Survival", xlab="Months from surgery", legend.labs=c("CAP & ctDNA(-)", "CAP & ctDNA(+)","GemCis & ctDNA(-)", "GemCis & ctDNA(+)"), legend.title="")
summary(KM_curve, times= c(0, 12, 24))
circ_data$ctDNA.C8D1.Chemo <- factor(circ_data$ctDNA.C8D1.Chemo, levels=c("1","2","3","4"))
cox_fit <- coxph(surv_object ~ ctDNA.C8D1.Chemo, data=circ_data) 
ggforest(cox_fit,data = circ_data) 
summary(cox_fit)
cox_fit_summary <- summary(cox_fit)
```


#Prognostic role of ctDNA C5D1 on Primary Site - 4 groups
```{r}
rm(list=ls())
setwd("~/Downloads") 
circ_data <- read.csv("ASAN_ClinicalData_GL_082023.csv")
circ_data <- subset(circ_data, !is.na(ctDNA.C5D1))
circ_data$RFS.months=circ_data$RFS.months-2
circ_data <- circ_data[circ_data$RFS.months>=0,]
circ_data$RFS.months[circ_data$RFS.months > 60] <- 60


circ_data$ctDNA.C5D1.PrimSite <- NA #first we create the variable for the ctDNA & NAC combination, and we assign values
circ_data <- circ_data %>%
  mutate(ctDNA.C5D1.PrimSite = case_when(
    PrimSite == "dCCA" & ctDNA.C5D1 == "NEGATIVE" ~ 1,
    PrimSite == "dCCA" & ctDNA.C5D1 == "POSITIVE" ~ 2,
    PrimSite == "pCCA" & ctDNA.C5D1 == "NEGATIVE" ~ 3,
    PrimSite == "pCCA" & ctDNA.C5D1 == "POSITIVE" ~ 4
  ))
circ_datadf <- as.data.frame(circ_data)

survfit(Surv(time = circ_data$RFS.months, event = circ_data$RFS.Event)~ctDNA.C5D1.PrimSite, data = circ_data)
event_summary <- circ_data %>%
  group_by(ctDNA.C5D1.PrimSite) %>%
  summarise(
    Total = n(),
    Events = sum(RFS.Event),
    Fraction = Events / n(),
    Percentage = (Events / n()) * 100
  )
print(event_summary)
surv_object <-Surv(time = circ_data$RFS.months, event = circ_data$RFS.Event)
KM_curve <- survfit(surv_object ~ ctDNA.C5D1.PrimSite, data = circ_data,conf.int=0.95,conf.type="log-log") 
ggsurvplot(KM_curve, data = circ_data, pval = FALSE, conf.int = FALSE, risk.table = TRUE, break.time.by=12, palette=c("blue","green","purple", "red"), title="DFS - ctDNA C5D1 & Primary Site", ylab= "Disease Free Survival", xlab="Months from surgery", legend.labs=c("dCCA & ctDNA(-)", "dCCA & ctDNA(+)","pCCA & ctDNA(-)", "pCCA & ctDNA(+)"), legend.title="")
summary(KM_curve, times= c(0, 12, 24))
circ_data$ctDNA.C5D1.PrimSite <- factor(circ_data$ctDNA.C5D1.PrimSite, levels=c("1","2","3","4"))
cox_fit <- coxph(surv_object ~ ctDNA.C5D1.PrimSite, data=circ_data) 
ggforest(cox_fit,data = circ_data) 
summary(cox_fit)
cox_fit_summary <- summary(cox_fit)
```


#Prognostic role of ctDNA C8D1 on Primary Site - 4 groups
```{r}
rm(list=ls())
setwd("~/Downloads") 
circ_data <- read.csv("ASAN_ClinicalData_GL_082023.csv")
circ_data <- subset(circ_data, !is.na(ctDNA.C8D1))
circ_data$RFS.months=circ_data$RFS.months-2
circ_data <- circ_data[circ_data$RFS.months>=0,]
circ_data$RFS.months[circ_data$RFS.months > 60] <- 60


circ_data$ctDNA.C8D1.PrimSite <- NA #first we create the variable for the ctDNA & NAC combination, and we assign values
circ_data <- circ_data %>%
  mutate(ctDNA.C8D1.PrimSite = case_when(
    PrimSite == "dCCA" & ctDNA.C8D1 == "NEGATIVE" ~ 1,
    PrimSite == "dCCA" & ctDNA.C8D1 == "POSITIVE" ~ 2,
    PrimSite == "pCCA" & ctDNA.C8D1 == "NEGATIVE" ~ 3,
    PrimSite == "pCCA" & ctDNA.C8D1 == "POSITIVE" ~ 4
  ))
circ_datadf <- as.data.frame(circ_data)

survfit(Surv(time = circ_data$RFS.months, event = circ_data$RFS.Event)~ctDNA.C8D1.PrimSite, data = circ_data)
event_summary <- circ_data %>%
  group_by(ctDNA.C8D1.PrimSite) %>%
  summarise(
    Total = n(),
    Events = sum(RFS.Event),
    Fraction = Events / n(),
    Percentage = (Events / n()) * 100
  )
print(event_summary)
surv_object <-Surv(time = circ_data$RFS.months, event = circ_data$RFS.Event)
KM_curve <- survfit(surv_object ~ ctDNA.C8D1.PrimSite, data = circ_data,conf.int=0.95,conf.type="log-log") 
ggsurvplot(KM_curve, data = circ_data, pval = FALSE, conf.int = FALSE, risk.table = TRUE, break.time.by=12, palette=c("blue","green","purple", "red"), title="DFS - ctDNA C8D1 & Primary Site", ylab= "Disease Free Survival", xlab="Months from surgery", legend.labs=c("dCCA & ctDNA(-)", "dCCA & ctDNA(+)","pCCA & ctDNA(-)", "pCCA & ctDNA(+)"), legend.title="")
summary(KM_curve, times= c(0, 12, 24))
circ_data$ctDNA.C8D1.PrimSite <- factor(circ_data$ctDNA.C8D1.PrimSite, levels=c("1","2","3","4"))
cox_fit <- coxph(surv_object ~ ctDNA.C8D1.PrimSite, data=circ_data) 
ggforest(cox_fit,data = circ_data) 
summary(cox_fit)
cox_fit_summary <- summary(cox_fit)
```

